research paper topics on knowledge management

• Browse All Articles
• Newsletter Sign-Up

KnowledgeManagement →

No results found in working knowledge.

• Were any results found in one of the other content buckets on the left?
• Try removing some search filters.
• Use different search filters.

A descriptive framework for the field of knowledge management

• Regular Paper
• Published: 16 July 2020
• Volume 62 , pages 4481–4508, ( 2020 )

Cite this article

• Yousra Harb   ORCID: orcid.org/0000-0002-0906-9165 1 &
• Emad Abu-Shanab 2  

1355 Accesses

15 Citations

Explore all metrics

Despite the extensive evolution of knowledge management (KM), the field lacks an integrated description. This situation leads to difficulties in research, teaching, and learning. To bridge this gap, this study surveys 2842 articles from top-ranked KM journals to provide a descriptive framework that guides future research in the field of knowledge management. This study also seeks to provide a comprehensive depiction of current research in the field and categorizes these research activities into higher-level categories using grounded theory approach and topic modeling technique. The results show that KM studies are classified into four core research categories: technological, business, people, and domains/applications dimensions. An additional concern addressed in this study is the major research methodologies used in this field. The results raise awareness of the development of KM discipline and hold implications for research methodologies and research trends in the selected KM journals. The results obtained from this study also provide practitioners with a useful quality reference source. The framework and the components included provide researchers, practitioners, and educators with an ontology of KM topics, where they can cover deficiencies in research and provide an agenda for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Knowledge Management in Organizations - A Bibliometric Analysis of Research Trends

Knowledge Management: (Potential) Future Research Directions

The Pervasive Identity of Knowledge Management: Consolidation or Dilution?

Khasawneh R, Abu-Shanab E (2013) Using KMS as a tool to improve decision making process. In: Saeed S, Alsmadi I (eds) Knowledge-based processes in software development. IGI Global, New York, pp 1–11

Google Scholar  

Moro S, Cortez P, Rita P (2015) Business intelligence in banking: a literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Syst Appl 42(3):1314–1324

Delen D, Crossland MD (2008) Seeding the survey and analysis of research literature with text mining. Expert Syst Appl 34(3):1707–1720

Bragge J, Korhonen P, Wallenius H, Wallenius J (2012) Scholarly communities of research in multiple criteria decision making: a bibliometric research profiling study. Int J Inf Technol Decis Mak 11(2):401–426

MATH   Google Scholar  

Serenko A, Bontis N (2013) Global ranking of knowledge management and intellectual capital academic journals: 2013 update. J Knowl Manag 17(2):307–326

Mariano S, Awazu Y (2016) Artifacts in knowledge management research: a systematic literature review and future research directions. J Knowl Manag 20(6):1333–1352

Handzic M (2015) A descriptive analysis of knowledge management research: period from 1997 to 2012. In: Bolisani E, Handzic M (eds) Advances in knowledge management. Knowledge management and organizational learning, vol 1. Springer, Cham, pp 45–64

Walter C, Ribière V (2013) A citation and co-citation analysis of 10 years of KM theory and practices. Knowl Manag Res Pract 11(3):221–229

Strauss A, Corbin J (eds) (1990) Basic of qualitative research: grounded theory procedures and techniques. Sage, Newbury Park

Fteimi N (2015) Analyzing the literature on knowledge management frameworks: towards a normative knowledge management classification schema. In: Proceedings of 23rd European conference on information systems (ECIS), Münster

Abdullah R, Selamat MH, Sahibudin S, Alias RA (2005) A framework for knowledge management system implementation in collaborative environment for higher learning institution. J Knowl Manag Pract 6(1):1–8

Meso P, Smith R (2005) A resource-based view of organizational knowledge management systems. J Knowl Manag 4(3):224–234

Fteimi N, Lehner F (2016) Main research topics in knowledge management: a content analysis of ECKM publications. Electron J Knowl Manag 14(1):5–17

Serenko A, Bontis N (2013) The intellectual core and impact of the knowledge management academic discipline. J Knowl Manag 17(1):137–155

Serenko A, Dumay J (2015) Citation classics published in knowledge management journals. Part I: articles and their characteristics. J Knowl Manag 19(2):401–431

Serenko A, Bontis N, Booker L, Sadeddin K, Hardie T (2010) A scientometric analysis of knowledge management and intellectual capital academic literature (1994–2008). J Knowl Manag 14(1):3–23

Wallace DP, Fleet C, Downs LJ (2011) The research core of the knowledge management literature. Int J Inf Manage 31(1):14–20

Dwivedi YK, Venkitachalam K, Sharif AM, Al-Karaghouli W, Weerakkody V (2011) Research trends in knowledge management: analyzing the past and predicting the future. Inf Syst Manag 28(1):43–56

Ma Z, Yu K (2010) Research paradigms of contemporary knowledge management studies: 1998–2007. J Knowl Manag 14(2):175–189

Webster J, Watson R (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26(2):8–23

Guzzo RA, Jackson SE, Katzell RA (1987) Meta-analysis analysis. Res Organ Behav 9(1):407–442

King WR, He J (2005) Understanding the role and methods of meta-analysis in IS research meta-analysis in is research. Commun Assoc Inf Syst 16(October):665–686

Bentley RA (2008) Random drift versus selection in academic vocabulary: an evolutionary analysis of published keywords. PLoS ONE 3(8):3057

Yang H, Tate M (2012) A descriptive literature review and classification of cloud computing research. Commun Assoc Inf Syst 31(2):35–60

Martin P, Turner B (1986) Grounded theory and organizational research. J Appl Behav Sci 22(2):141–157

Wolfswinkel JF, Furtmueller E, Wilderom CP (2013) Using grounded theory as a method for rigorously reviewing literature. Eur J Inf Syst 22(1):45–55

Lawrence JE, Tar UA (2018) Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educ Med Int 55(1):79–105

Orlikowski W (1993) CASE tools as organizational change: investigating incremental and radical changes in systems development. MIS Q 17(3):309–340

Peng Y et al (2008) A descriptive framework for the field of data mining and knowledge discovery. Int J Inf Technol Decis Mak 7(4):639–682

Bacon CJ, Fitzgerald B (2001) Framework for the field of information systems. ACM SIGMIS Datab DATABASE Adv Inf Syst 32(2):46–67

Blei D, Ng A, Jordan M (2003) Latent Dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

Eickhoff M, Wieneke R (2018) Understanding topic models in context: a mixed-methods approach to the meaningful analysis of large document collections. In: Proceedings of the 51st Hawaii international conference on system sciences, Hawaii, pp 903–912

Janasik N, Honkela T, Bruun H (2009) Text mining in qualitative research: application of an unsupervised learning method. Organ Res Methods 12(3):436–460

Baumer E, Mimno D, Guha S, Quan E, Gay G (2017) Comparing grounded theory and topic modeling: extreme divergence or unlikely convergence? J Assoc Inf Sci Technol 68(6):1397–1410

Al-Ramahi M, El-Gayar O, Liu J (2016) Discovering design principles for persuasive systems: a grounded theory and text mining approach. In: Proceedings of 2016 49th Hawaii international conference on system sciences (HICSS). IEEE, Koloa, pp 3074–3083

Lin YC, Hsu CL, Lin MH, Yang HF, Hong CF (2011) Using grounded theory and text mining to find interesting reading materials for slow EFL learners. In: International conference on human–computer interaction. Springer, Berlin, pp 77–85

Corbin J, Strauss A (1990) Grounded theory research: procedures, canons, and evaluative criteria. Qual Sociol 13(1):3–21

Chiovitti R, Piran N (2003) Rigour and grounded theory research. J Adv Nurs 44(4):427–435

Yu CH, Jannasch-Pennell S, DiGangi A (2011) Compatibility between text mining and qualitative research in the perspectives of grounded theory, content analysis, and reliability. Qual Rep 16(3):730–744

Lin FR, Hsieh LS, Chuang FT (2009) Discovering genres of online discussion threads via text mining. Comput Educ 52(2):481–495

Haddi E, Liu X, Shi Y (2013) The role of text pre-processing in sentiment analysis. Proc Comput Sci 17(2013):26–32

Wang X, Mccallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 424–433

Bird S, Klein E, Loper E (eds) (2009) Natural language processing with python, 1st edn. O’Reilly Media Inc., New York

Ngulube P (2015) Trends in research methodological procedures used in knowledge management studies. Afr J Lib Arch Inf Sci 25(2):125–143

Creswell JW (ed) (2013) Qualitative inquiry and research design: choosing among five approaches, 3rd edn. Sage, Los Angeles

Myers MD (ed) (2009) Qualitative research in business and management. Sage, London

Abu-Shanab E, Harb Y (2019) E-government research insights: text mining analysis. Electron Commer Res Appl 38(November–December):100892

Lau JH, Grieser K, Newman D, Baldwin T (2011) Automatic labelling of topic models. In: Proceedings of the 49th annual meeting of the association for computational linguistics, Portland, Oregon, pp 1536–1545

Velcin J et al (2011) Readitopics: make your topic models readable via labeling and browsing. In: Proceedings of IJCAI: international joint conference on artificial intelligence, pp 5874–5876

Chang J, Boyd-Graber J, Gerrish S, Wang C, Blei D (2009) Reading tea leaves: how humans interpret topic models. In: Proceedings of advances in neural information processing systems. Vancouver, BC, pp 288–296. http://umiacs.umd.edu/~jbg//docs/nips2009-rtl.pdf

Abu-Shanab E, Knight MB, Haddad M (2014) Knowledge sharing practices and the learning organization: a study. IUP J Knowl Manag 12(2):16–27

Eriksson P, Kovalaine A (eds) (2008) Grounded theory research. Qualitative methods in business research. Sage, Thousand Oaks

Intezari A, Gressel S (2017) Information and reformation in KM systems: big data and strategic decision-making. J Knowl Manag 21(1):71–91

Khan Z, Vorley T (2017) Big data text analytics: an enabler of knowledge management. J Knowl Manag 21(1):18–34

Yang JT (2007) The impact of knowledge sharing on organizational learning and effectiveness. J Knowl Manag 11(2):83–90

MathSciNet   Google Scholar  

Lopes I, Rodrigues A (2005) Intangible assets identification and valuation—a theoretical framework approach to the portuguese airlines companies. Electron J Knowl Manag 5(2):193–202

Evangelou C, Karacapilidis N (2005) On the interaction between humans and knowledge management systems: a framework of knowledge sharing catalysts. Knowl Manag Res Pract 3(4):253–261

Lopes I, Martins M, Nunes M (2005) Towards the knowledge economy: the technological innovation and education impact on the value creation process. Electron J Knowl Manag 3(2):129–138

Diefenbach T (2006) Intangible resources: a categorial system of knowledge and other intangible assets. J Intell Cap 7(3):406–420

Preece A et al (2001) Better knowledge management through knowledge engineering. IEEE Intell Syst 16(1):36–43

Mohammadi K, Khanlari A, Sohrabi B (2010) Organizational readiness assessment for knowledge management. Int J Knowl Manag (IJKM) 5(1):29–45

Goh S (2002) Managing effective knowledge transfer: an integrative framework and some practice implications. J Knowl Manag 6(1):23–30

Jeenger P, Kant R (2013) Understanding the knowledge sharing barriers in organisation: a fuzzy AHP approach. J Inf Knowl Manag 12(1):1350003

Alavi M, Leidner D (2001) Review: knowledge management and knowledge management systems: conceptual foundation and research issues. MIS Q 25(1):107–136

Biloslavo R, Zornada M (2004) Development of a knowledge management framework within the systems context. In: Proceedings of the 5th European conference on organizational knowledge, learning and capabilities, 2–3 April, Innsbruck

Sureephong P, Chakpitak N, Ouzrout Y, Bouras A (2008) An ontology-based knowledge management system for industry clusters. In: Proceedings of global design to gain a competitive edge. Springer, London, pp 333–342

Mentzas G, Apostolou D (1998) Towards a holistic knowledge leveraging infrastructure: the KnowNet approach. In: Proceedings of the 2nd international conference on practical aspects of knowledge management, Basel, pp 1–12

Asrar-ul-haq M, Anwar S (2016) A systematic review of knowledge management and knowledge sharing: trends, issues, and challenges. Cog Bus Manag 14(1):1–17

Nonaka I, Takeuchi H (eds) (1995) The knowledge-creating company: how Japanese companies create the dynamics of innovation. Oxford University Press, New York

Ahmed Y, Ahmad M, Ahmad N, Zakaria NH (2018) Social media for knowledge-sharing: a systematic literature review. Telemat Inform 37(February 2019):72–112

Charband Y, Navimipour NJ (2016) Online knowledge sharing mechanisms: a systematic review of the state of the art literature and recommendations for future. Inf Syst Front 18(6):1131–1151

Singh M, Kant R (2008) Knowledge management barriers: an interpretive structural modeling approach. Int J Manag Sci Eng Manag 3(2):141–150

Ferraris A, Mazzolen A, Devalle A, Couturier J (2018) Big data analytics capabilities and knowledge management: impact on firm performance. Manag Decis 57(8):1923–1936

Hasani K, Sheikhesmaeili S (2016) Knowledge management and employee empowerment. Kybernetes 45(2):337–355

Attia A, Salama I (2018) Knowledge management capability and supply chain management practices in the Saudi food industry. Bus Process Manag J 24(2):459–477

Im Y, Park J, Kim M, Park K (2019) Comparative study on perceived trust of topic modeling based on affective level of educational text. Appl Sci 9(21):4565

Hussinki H, Ritala P, Vanhala M, Kianto A (2017) Intellectual capital, knowledge management practices and firm performance. J Intell Cap 18(4):904–922

Barão A, de Vasconcelos JB, Rocha A, Pereira R (2017) A knowledge management approach to capture organizational learning networks. Int J Inf Manag 37(6):735–740

Gupta V, Chopra M (2018) Gauging the impact of knowledge management practices on organizational performance—a balanced scorecard perspective. VINE J Inf Knowl Manag Syst 48(1):21–46

Torres A, Ferraz S, Santos-Rodrigues H (2018) The impact of knowledge management factors in organizational sustainable competitive advantage. J Intell Cap 19(2):453–472. https://doi.org/10.1108/JIC-12-2016-0143

Article   Google Scholar  

Abualoush SH, Obeidat AM, Tarhini A, Masa’deh R, Al-Badi A (2018) The role of employees’ empowerment as an intermediary variable between knowledge management and information systems on employees’ performance. VINE J Inf Knowl Manag Syst 48(2):217–237

Durst S, Pietro E (2018) Exploring knowledge management practices in third-party logistics service providers. VINE J Inf Knowl Manag Syst 48(2):162–177

Tsang YP et al (2018) A fuzzy association rule-based knowledge management system for occupational safety and health programs in cold storage facilities. VINE J Inf Knowl Manag Syst 48(2):199–216

Lejla T, Nijaz B (2018) Information technology, knowledge management and human resource management. VINE J Inf Knowl Manag Syst 48(2):255–276

Hwang Y, Lin H, Shin D (2018) Knowledge system commitment and knowledge sharing intention: the role of personal information management motivation. Int J Inf Manag 39(April):220–227

Daňa J, Caputo F, Ráček J (2018) Complex network analysis for knowledge management and organizational intelligence. J Knowl Econ 11:405–424

Download references

Author information

Authors and affiliations.

Business Information Technology, Yarmouk University, Irbid, Jordan

Yousra Harb

Accounting and Information Systems, Qatar University, Doha, Qatar

Emad Abu-Shanab

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yousra Harb .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Table  4 .

Rights and permissions

Reprints and permissions

About this article

Harb, Y., Abu-Shanab, E. A descriptive framework for the field of knowledge management. Knowl Inf Syst 62 , 4481–4508 (2020). https://doi.org/10.1007/s10115-020-01492-x

Download citation

Received : 22 November 2019

Accepted : 06 July 2020

Published : 16 July 2020

Issue Date : December 2020

DOI : https://doi.org/10.1007/s10115-020-01492-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Knowledge management
• Research direction
• Grounded theory
• Topic modeling
• Text mining
• Find a journal
• Publish with us
• Track your research

ORIGINAL RESEARCH article

Influence of knowledge management practices on entrepreneurial and organizational performance: a mediated-moderation model.

• 1 School of Management, Jiangsu University, Zhenjiang, China
• 2 Lyallpur Business School, Government College University Faisalabad, Faisalabad, Pakistan
• 3 Government College Women University, Faisalabad, Pakistan
• 4 Ghazi University, Dera Ghazi Khan, Pakistan

This study aims to identify the influence of knowledge management practices on the entrepreneurial and organizational performance with the mediating effect of dynamic capabilities and moderating role of opportunity recognition. Data were gathered from 486 entrepreneurs and applied a structural equation model to test the hypotheses. We found that knowledge management practices have a positive and significant influence on dynamic capabilities, as well as have a significant impact on entrepreneurial and organizational performance. Moreover, results indicated that dynamic capabilities partially mediate in the relationship between knowledge management practices on entrepreneurial and organizational performance. Furthermore, the relationship between knowledge management practices with entrepreneurial and organizational performance strengthening by opportunity recognition. Further, implications and limitations were discussed in the paper.

Introduction

With the rapid development in the knowledge-based economy, knowledge is considered an important measure to create prosperity and success ( Abubakar et al., 2019 ). Knowledge is the best driving force for entrepreneurial and organizational performance and its success ( Zaim et al., 2019 ). According to Wahda (2017) knowledge is the essential element of an organization for achieving a competitive advantage and maximum outcome. Knowledge management is defined as the explicit and effective management of important knowledge and its related practices of identification and its exploitation ( Ngah et al., 2016 ). Effective knowledge resources make up knowledge capability among organizations with the help of knowledge sharing, knowledge creation, innovativeness, and knowledge absorption. Therefore, when these resources merged it determine the knowledge management practices which ultimately turn into the relationship with organizational performance ( Alaarj et al., 2016 ).

Meanwhile, Butt et al. (2019) argue that organizations effort to look for means that support the workforce of knowledge resources to accomplish with the organization’s challenges in a competitive market as well as enhanced the entrepreneurial and organizational performance. Prior researchers indicate that knowledge management practices have progressively become an interest of topic in all areas of business studies and provide a significant role in the entrepreneurial and organizational success because of its growing awareness in the society ( Tang, 2017 ). Therefore, Antunes and Pinheiro (2020) suggested that knowledge management practices would help in the development of small and medium enterprises (SME’s) and their activities so they become more strong and effective to stay longer. Looking into previous literature researchers examined the role of knowledge management practices on organizational performance and found that knowledge management positively related to organizational and business performance ( Cerchione and Esposito, 2016 ; Serrat, 2017 ; Abuaddous et al., 2018 ).

Moreover, knowledge-based theory (KBT) explains that when knowledge management practices are effectively and efficiently managed, it develops unique capabilities that contribute to enhanced organizational performance by innovation ( Kane, 2017 ). Therefore, organizations with superior knowledge management practices are likely to achieve organizational performance ( Lopes et al., 2017 ; Shujahat et al., 2019 ). Akhavan et al. (2016) state that knowledge management practices such as knowledge sharing, knowledge acquisition, and knowledge application contributes to innovation which helps to improve organizational performance.

Furthermore, Byukusenge and Munene (2017) explain that knowledge sharing is an activity through knowledge skills, information is exchanged among people, peers, friends, or with in the organizations. Moreover, Centobelli et al. (2019) specified that innovative capacity refers to the innovation that involves the transformation of an effect into a reality that develops a new product and service that meets the needs and demands of the customers in the organizations. Researchers Santoro et al. (2018) explained that capacity as the organization’s ability to value, integrate, and apply new knowledge for improving the organizational performance. However, the relationship between knowledge sharing, innovative capacity, and absorptive capacity and organizational performance has been examined in the prior literature in the context of Western culture ( Lopes et al., 2017 ).

Furthermore, existing studies suggested that dynamic capability playing a vital role in achieving organizational and business firm performance through sensing, knowledge sharing, and reconfiguring ( Mardani et al., 2018 ; Antunes and Pinheiro, 2020 ). Prior researchers confirmed that dynamic capability had a direct and indirect positive influence on firm performance ( Lin and Wu, 2014 ). Numerous researchers found that dynamic capability had a positive effect on organizational performance ( Hung et al., 2010 ). Each of these studies examined the dynamic capability as a predictor variable to measure business and organizational performance and the relationship between knowledge management practices and its impact on organizational and entrepreneurial performance is under-explored. Therefore, it is necessary to identify the direct effect of knowledge management practices and the indirect effect of dynamic capability on entrepreneurial and organizational performance.

The gap of the study consists of four perspectives. Firstly, this study covers the existing gap in the literature of knowledge management practices such as knowledge sharing, innovative capacity, and absorptive capacity on organizational and entrepreneurial performance, because no empirical study is so far available on this relationship. Secondly, this study measures the performance of SME entrepreneurs using dynamic capability as a mediator because the significance of the SME sector is increasing gradually. Thirdly, most of the previous studies focused on the other sectors as well as examined the role of knowledge management practices on business performance ( Hung et al., 2010 ; Protogerou et al., 2012 ; Gholami et al., 2013 ) and taken innovation as a mediator variable in the relationship between organizational performance and other factors such as organizational learning, entrepreneurial orientation ( Hartono and Halim, 2014 ; Ferreira et al., 2020a ). Therefore, the relationship between knowledge management practices using dynamic capability as a mediator on entrepreneurial and organizational performance of SMEs is the motivation of this study. Fourthly, the direct relationship of dynamic capability on organizational and entrepreneurial performance is defined in the literature ( Ambrosini and Bowman, 2009 ). It is seen in the previous researches the relationship between opportunity recognition and dynamic capability on entrepreneurial and organizational performance is neglected by the researchers because opportunity recognition realizes an idea, capability that matches well with a particular target market to improve business performance. Thus, this study takes opportunity recognition as a moderating variable in the relationship between dynamic capabilities, entrepreneurial and organizational performance.

Literature Review and Hypotheses Development

Knowledge management.

Researchers believe that firms can stand out in one or more value-added disciplines; it can achieve unique competitive advantages and excellent organizational performance ( Torabi and El-Den, 2017 ). Knowledge management is likely to be a value-added method, more actively using knowledge and expertise to create value and improve organizational efficiency ( Rašula et al., 2012 ). Organizations with a higher level of knowledge management capabilities are more likely to increase the competitiveness of an entrepreneur by collecting, organizing, and transforming knowledge to implement ( Shujahat et al., 2019 ). Therefore, knowledge management practices play an important role not only in the firm’s performance but also lead to entrepreneurial performance. The process of knowledge management operation in an organization is complex and the entrepreneurs are managing, respectively. Thus, this study focuses on the key practices which the organizations acquire and use to improve their knowledge.

Relationship Between Knowledge Sharing Capacity, Dynamic Capability, Entrepreneurial and Organizational Performance

In the current era of a knowledge-based economy, knowledge plays an important role in driving the value of an organization. Individuals with valued knowledge help to achieve and extend the organizational performance that ultimately contributes to the sustainability of the organizations ( Ha and Lo, 2018 ). Therefore, organizations with a lack of knowledge sharing capacities not performed well in competitive markets. Prior researches stated that entrepreneurs participated in the development and sharing of valuable knowledge, that can not only improve entrepreneurial performance as well as enhance the organizational performance ( Ohemeng and Kamga, 2020 ). Knowledge sharing capacity assists in problem-solving, adopting new technology, creating an invention, and enhancing the dynamic capabilities of an organization ( Ali et al., 2019 ).

The knowledge-sharing capacity of an entrepreneur develops the dynamic capability for getting competitive advantages ( Liao et al., 2007 ). The researchers argued that knowledge sharing helps the dynamic capability of an individual and organization to develop new products, engage the entrepreneur to absorb the change, show willingness for competitive advantages ( Carmeli et al., 2013 ; Kang and Lee, 2017 ). Moreover, Lin and Wu (2014) explored that dynamic capability is the combination of designed structure and learning of different activities, which helps the entrepreneur and organization in daily routine work. Dynamic capability helps in managing the inner capacities of an organization and assists in performance. Therefore, knowledge management is not enough to enhance performance until considering knowledge sharing as a dynamic capability in relation to entrepreneurial and organizational performance ( Rafique et al., 2018 ). Therefore, this study posited that;

H1a: Knowledge sharing capacity has a positive influence on dynamic capability.

H1b: Knowledge sharing capacity has a positive influence on entrepreneurial performance.

H1c: Knowledge sharing capacity has a positive influence on organizational performance.

Relationship Between Innovative Capacity, Dynamic Capability, Entrepreneurial and Organizational Performance

Innovative capacity is considered as an important factor to innovate something new or different ( Furman et al., 2002 ). In the context of innovative capacity, the use of skills to create new ideas with an association of vision and capabilities ( Lawson and Lorenz, 1999 ). Every organization plans to start a new corporation with a unique approach, the challenge is not only to discover an excellent idea but also to invent an opportunity that helps the entrepreneur to build with innovative capacity ( Halkos and Skouloudis, 2018 ). There are less empirical researches proves that innovative capacity and organizational performance growth parallel ( Hernández-Perlines et al., 2019 ). Gieske et al. (2016) argue that innovative capacity is based on human and capital resources; it also depends on the overall infrastructure of the organization and the combination of a proactive and innovative environment. The process of commercialization of an organization has interacted through innovative capacity, which directly affects and increases the percentage of organizational performance in the market.

The absorption of external knowledge prepares the entrepreneur to increase the innovative capacity ( Wu et al., 2017 ). Innovative capacity determined the organizational culture, leadership characteristics, procedure of product invention, and the use of strategies in launching new products with organizational performance ( Proksch et al., 2017 ). Many studies have been conducted to consider the role of innovative capacity and its relation with dynamic capability in organizational performance ( Najmi et al., 2018 ). Organizations with innovative capacity and proactive behavior change the business environment to improve performance ( Zhou et al., 2019 ). Furthermore, researchers explored that innovative capacity raises the energy level of an organization, which positively influences on organizational performance ( Fainshmidt et al., 2016 ).

Ferreira et al. (2020b) described innovation is the process to improve and launch a new product in the market, enhance product quality and productivity through the development of the manufacturing process and its adoption. García-Sánchez et al. (2018) explained that as the level of innovative capacity becomes higher; it gives an edge to the entrepreneurial performance by using dynamic capabilities. The entrepreneur utilizes dynamic capabilities to absorb innovation for competitive advantages. Moreover, the innovative capacity differentiates entrepreneurs and organizations across the market due to their competitive dynamic capabilities. The innovative capacity and dynamic capability associate to attain the performance in a professional setting ( Liu et al., 2018 ). Considering the innovative capacity as a vital dynamic capability lead toward the entrepreneurial and organizational performance of textile-based SMEs, this study hypothesized that;

H2a: Innovative capacity has a positive impact on dynamic capability.

H2b: Innovative capacity has a positive influence on entrepreneurial performance.

H2c: Innovative capacity has a positive influence on organizational performance.

Relationship Between Absorptive Capacity, Dynamic Capability, Entrepreneurial and Organizational Performance

Absorptive capacity assists the entrepreneurs in understanding and utilizing valuable information, to build marketing strategies, which generate long term financial profit and increase the performance ( Kale et al., 2019 ). The significant relationship between absorptive capacity and dynamic capability has been proved by Latukha and Veselova (2019) and further included the process of evaluation and adaptation for entrepreneurial performance in an organization. Liu et al. (2020) proposed that imminent absorptive capacity and comprehended absorptive capacity are essential, rather than adequate, and to attain competitive organizational benefits, both expected and comprehended capability plays a significant role in enhancing the performance. Absorptive capability is a blend of potential absorptive capability and comprehended absorptive capability, and is known as potential competency, which permits an organization to increase, assimilate, integrate, transfer and utilize new knowledge for the organizational and entrepreneurial performance ( Chaudhary and Batra, 2018 ).

Furthermore, Ahn et al. (2016) proposed that the firm’s absorptive capacity plays a beneficial role in the research and development activities and organizational learning of the firms. Therefore, the firms with a high level of absorptive capacity lead the firms to enhance their innovation performance. Additionally, Xue et al. (2019) asserted that the firm’s absorptive capacity is considered to be critical to the firm’s innovative capabilities. Ince et al. (2016) endorsed the positive influence of absorptive capacity on dynamic capability, which improves entrepreneurial skills. The absorptive capacity allows entrepreneurs or organizations to absorb internal and external knowledge, which is necessary to gain ideas and implications for performance strategies. Few studies focused on the firms’ absorptive capacity in deriving technological information from external means and how it contributes to organizational skills and activities ( Verma et al., 2017 ; Chaudhary, 2019 ). Absorptive capacity is not only a base for organizational performance, but other factors are also involved, such as entrepreneurial performance ( Rangus and Slavec, 2017 ). Therefore, absorptive capacity has been considered as an important part of dynamic capability, which boosts the performance of textile-based SMEs.

H3a: Absorptive capacity has a positive impact on dynamic capability.

H3b: Absorptive capacity has a positive influence on entrepreneurial performance.

H3c: Absorptive capacity has a positive influence on organizational performance.

Relationship Between Dynamic Capability, Entrepreneurial and Organizational Performance

Dynamic capability is the part of the entrepreneurial restructuring and environmental changes, which is directly linked with its performance. In high-tech firms, the dynamic capabilities of an entrepreneur are the most reliable and sound source for taking advantage ( Jiang et al., 2018 ). Raza et al. (2018) dynamic capabilities cover sensing, reconfiguring, and seizing capability of a performance organization. The dynamic capabilities of an organization guide in utilizing valuable resources during the performance ( Zhou et al., 2019 ). Moreover, dynamic capability help to innovate a new product accepts to create and show its willingness to achieve competitive advantage through knowledge sharing behavior. In some organizations, employees are afraid to share knowledge with entrepreneurs and other colleagues to hinder the progress of other co-workers ( Falasca et al., 2017 ). Prior researchers believed that, once the discouraging knowledge sharing behavior establish in an organization environment, it will be unfavorable, difficult to change ( Ha and Lo, 2018 ; Ferreira et al., 2020a ).

Looking into previous studies resource-based theory explored the relationship between the dynamic capability of an entrepreneur and entrepreneurial performance ( Battisti and Deakins, 2017 ; Wang and Kim, 2017 ). The dynamic capability of an entrepreneur assists in facing new challenges, exploring opportunities to maintain and develop organizational performance. The decision-making power and dynamic capability of an organization with market strategies enhance innovative capacity, which assists in-process and technological innovation ( Rafique et al., 2018 ). The researcher suggested that procedure of attaining, developing, distributing, and providing services from dealers to customers with dynamic organizational capabilities enhance organizational performance ( Pezeshkan et al., 2016 ). Moreover, the organization requires peripheral resources to supplement the inefficiency of their internal skills and actions with dynamic capability for organizational performance ( Bamel and Bamel, 2018 ).

Now a day’s many organizations are working on people as a resource for performance. The employee-driven force, with dynamic capability in an organization, plays a significant impact on competitive advantages and organizational performance ( Braganza et al., 2017 ). Organizations with dynamic capability overcome the competitor threats and block the competitor’s actions ( Likoum et al., 2020 ); it minimizes the expected competitor’s actions with potential adverse in organizational performance and facilitates the entrepreneurs and organization with idea creation. Therefore, the following hypotheses are proposed:

H4a: Dynamic capability has a positive impact on entrepreneurial performance.

H5a: Dynamic capability has a positive impact on organizational performance.

Mediating Effect of Dynamic Capability

Prior researchers argued that dynamic capability has a positive impact on organizational performance ( Xing et al., 2020 ). Dynamic capability helps to develop a new product by knowledge sharing capacity of the entrepreneur within the organization ( Wang and Kim, 2017 ). Knowledge sharing increases the knowledge resource with a considerable role of the dynamic capability to achieve a competitive advantage ( Kang and Lee, 2017 ). Researchers explored that innovative capacity raises the energy level of an organization, which positively influences performance ( Proksch et al., 2017 ). Moreover, organizations with a higher level of innovative capacity are more prone to perform well, and in a better position to recognize market opportunities ( Torabi and El-Den, 2017 ). The absorptive capacity of an entrepreneur absorbs the innovative technology and makes it feasible for an organization to accumulate the resources for objectives and competitive advantages ( Kale et al., 2019 ). Furthermore, in a similar context, absorptive capacity, and dynamic capability are found fundamental to organizational success ( Ferreira et al., 2020b ). Organizations with a higher absorptive capacity assist in learning from competitors with firm dynamic capabilities as well as demonstrate the knowledge in organizations for better performance ( Latukha and Veselova, 2019 ).

There is a considerable role in dynamic capability as a mediator between organizational performance and knowledge management practices. The proper utilization of dynamic capability is acquired knowledge, innovative, and absorptive capacities lead the performance of an entrepreneur and organization ( Likoum et al., 2020 ). Therefore, this study incorporates the mediating role of dynamic capability in the relationship between knowledge management practices such as knowledge sharing, innovative, and absorptive capacity with entrepreneurial and organizational performance. Therefore, the following hypotheses are proposed:

H4b: Dynamic capability mediates the relationship between knowledge sharing capacity and entrepreneurial performance.

H5b: Dynamic capability mediates the relationship between knowledge sharing capacity and organizational performance.

H4c: Dynamic capability mediates the relationship between innovative capacity and entrepreneurial performance.

H5c: Dynamic capability mediates the relationship between innovative capacity and organizational performance.

H4d: Dynamic capability mediates the relationship between absorptive capacity and entrepreneurial performance.

H5d: Dynamic capability mediates the relationship between absorptive capacity and organizational performance.

Relationship Between Entrepreneurial and Organizational Performance

Entrepreneurial performance is concerned with risk-taking and decision-making attitude, product invention for the organization, and market innovation ( Kantur, 2016 ). Entrepreneurial performance associated with the new values and creativity, time, resources, risks, and another ingredient toward organizational performance ( Miao et al., 2017 ). The prior studies show that entrepreneurial performance can lead the firm performance ( Chavez et al., 2017 ; Al-Henzab et al., 2018 ). Moreover, prior studies argued that entrepreneurial performance is an essential factor for the long term survival and development of the organization ( Hartono and Halim, 2014 ). Al-Dhaafri et al. (2016) found that entrepreneurial performance always has a positive influence on organizational performance and can help organizations to achieve competitive advantages. Furthermore, Filser and Eggers (2014) examined the role of entrepreneurial performance on organizational performance researching different countries such as Austria, Liechtenstein, and Switzerland, which found that entrepreneurial performance significantly influenced SME’s development. Thus, entrepreneurial performance enabling the achievement of organizational performance and propose the following hypothesis.

H6: Entrepreneurial performance has a positive impact on organizational performance.

The Moderating Role of Opportunity Recognition in the Relationship Between Entrepreneurial and Organizational Performance

Opportunity recognition proposed that the cognitive of different entrepreneur’s results are different in the entrepreneurial process and performance ( Hmieleski and Baron, 2008 ). Hasan et al. (2016) discussed the mediating role of opportunity recognition in association with entrepreneurial performance and found it as a critical factor in enhancing entrepreneurial performance. Furthermore, a large number of scholars suggested that self-made strategies of an entrepreneur play a significant role in the process of opportunity recognition ( Bagheri, 2017 ; Ploum et al., 2018 ). However, due to less focus by researchers on this crucial factor, we incorporate opportunity recognition in this study to measure its impact on the relationship between dynamic capability and entrepreneurial performance. Therefore, competitive advantages are important for entrepreneurs and also impact organizational performance until unless dynamic capabilities put through, and capabilities are important for performance ( Teece et al., 2016 ).

Opportunity recognition is to recognize the capabilities to attain the best source from the market for competitive advantages and entrepreneurial performance ( Teece, 2016 ). Entrepreneurial opportunities are renowned through circumstances that new goods, services, raw materials, and procedures could be offered and commercialized at advanced value than the production budget. There is a deficiency in opportunity recognition, concerning entrepreneurial performance ( George et al., 2016 ), and the efficacious entrepreneur always chooses appropriate opportunity with competences ( Kim et al., 2018 ), formerly and subsequently business ventures leads to the successful entrepreneurial performance. Opportunity recognition plays a vital role in entrepreneurial performance.

The opportunity for organizational performance, positive entrepreneur behavior, dynamic capabilities, market knowledge, positioning of services provide more opportunities to acquire the market to grow and survive ( Jantunen et al., 2005 ). The researchers argue that organizations with dynamic capabilities obtain more competitive advantages than other firms, and opportunity recognition gives a chance for better performance in product development and organizational performance ( Chirico and Nordqvist, 2010 ; Swoboda and Olejnik, 2016 ). However, there is less focus on SME’s empirical research related to the moderating role of opportunity recognition and its drivers in smaller organizations. Ferreira et al. (2020a) focused on the dynamic organizational capabilities in small organizations with opportunities for competitive advantages.

Sanz-Velasco (2006) argued that market interaction and entrepreneurs’ life experiences related to the market, industrial knowledge, and resources should be considered for opportunity recognition. The researchers proposed that an opportunity may have an impression of vaguely distinct market needs, which means that potential consumers may or may not have the capability to articulate their demands and interests ( Roundy et al., 2018 ; Li et al., 2020b ). The identification of the needs of a customer might lead to a prompt appearance of opportunity recognition, which is a result of better organizational performances ( Hu et al., 2018 ).

Besides, the researchers suggested that market potential influences the opportunity recognition in the process of product development ( Obschonka and Hahn, 2018 ; Neneh, 2019 ). Therefore, the idea of entrepreneurship is related to the process of evaluation, discovery, exploration, sources, and recognition of opportunities that highly influence the entrepreneurial and organizational performance ( Campos, 2017 ). Thus, this study postulates that better opportunity recognition would lead to higher organizational and entrepreneurial performance and formulate the following hypothesis:

H7a: Opportunity recognition positively moderates the relationship between dynamic capability and entrepreneur performance.

H7b: Opportunity recognition positively moderates the relationship between dynamic capability and organizational performance.

Materials and Methods

Sample and data collection.

The nature of this study was cross-sectional and data were collected through a convenience sampling technique. Figure 1 shows the conceptual model of the study. The target population was the SME’s of Pakistan because SME’s were considered as the backbone industry of Pakistan. Moreover, we selected big cities such as Lahore, Faisalabad, Sheikhupura, Karachi, Multan, and Sialkot of Pakistan for data collection. To avoid the issue of common method bias ( Podsakoff et al., 2012 ), we collected data in two rounds using the time-lag approach. In the first round, we collected data for knowledge management practices and dynamic capability measures. In the second round, we collected data for entrepreneurial and organizational performance and opportunity recognition. However, due to the unavailability of registered SME’s in Pakistan, we contacted small and medium chambers of commerce of every city to provide the list of SMEs, after getting the list from the chamber we contacted the SME’s owners through emails and personal visits.

Figure 1. Conceptual model.

Furthermore, we distributed 600 paper-pencil questionnaires to the respondents who positively respond to us on email and personal visits. We ensured them that this research is purely for academic purposes and the information will be confidential. The original draft of the questionnaire was in English and Urdu language because some of the SME’s owners were illiterate. Finally, in the initial screening, we received 508 questionnaires with a participation rate was 84.6% and 22 responses were dropped due to missing data. Thus, the final sample size was 486 responses. Among the valid responses, all the respondents were male and the age of respondents was starting from 18 years to 47 years and above. The highest age range of respondents was 33–39 (32.30%). Additionally, the highest work experience of the respondent was 1–5 years (26.13%) and the region of SMEs was Faisalabad, Lahore, Sialkot, Sheikhupura, Karachi, and Multan. The highest response rate was from Faisalabad 119 (24.48%) and the lowest response rate was from Sialkot 31 (6.37%).

To ensure the realistic and effective content of the research model, a structured questionnaire was compiled, and all exogenous variables were constructed and operationalized from the existing literature of knowledge sharing capacity, innovative capacity, absorptive capacity, dynamic capability, and opportunity recognition, entrepreneurial and organizational performance. To measure the 41 constructs, we used a 5-point Likert scale ranging from 1 strongly disagree to 5 strongly agree to quantify the results.

Knowledge Sharing Capacity

To measure knowledge sharing capacity five items were adapted from the study of Hsu et al. (2007) . This scale is widely accepted and used by previous researchers ( Davenport and Prusak, 1998 ; Keikha, 2018 ). A sample item, “I frequently participate in knowledge sharing activities.”

Innovative Capacity

To assess innovative capacity we have adopted five measurement constructs from the study of Hurley and Hult (1998) . A sample item “risk-taking is encouraged in our firm.”

Absorptive Capacity

To measure absorptive capacity four items were used developed by Leal-Rodríguez et al. (2014) . A sample item “our firm regularly considers the consequences of changing market demand in terms of new ways to provide services.”

Dynamic Capability

A dynamic capability was measured using two dimensions exploration and exploitation, with 3 items each. This scale was adapted from the study of Atuahene-Gima (2005) . This scale was used by previous researchers ( Ferreira et al., 2020a ). A sample item for exploration “acquired manufacturing technologies and skills entirely new the firm.” A sample item off exploitation “upgraded current knowledge and skills for familiar products and technologies.”

Opportunity Recognition

The five measurement items for opportunity recognition taken from the study of Kuckertz et al. (2017) . A sample item “my organization always alert to business opportunities.”

Entrepreneurial Performance

To measure entrepreneurial performance, we used eleven items scale developed by Colbert et al. (2008) . A sample item “entrepreneurs: forms goals, allocates resources to meet them, and monitors progress toward them.”

Organizational Performance

To examine organizational performance, four items were adopted from the study ( García-Morales et al., 2008 ). A sample item “return on assets.”

Data Analysis Technique

We used the partial least square structural equation modeling (PLS-SEM) technique to test the measurement model and structural model results. The Smart-PLS3 software is used to cover the flaws in the data and bring fluency in data results. This software is also used to estimate the causal and empirical model relationship between the variables as well as examine the correlation between constructs, respectively ( Hair et al., 2010 ). Nowadays this software is considered as a silver bullet in the field of management science research and used by several researchers to test the hypotheses results ( Hair et al., 2011 ; Li et al., 2020a ).

Measurement of Model

The fitness of the model was assessed through reliability and validity analysis. Table 1 shows the values for Cronbach’s alpha (CA), rho_A, the average value extracted (AVE), and composite reliability (CR). The values of convergent validity should be higher than the thrush hold values; rho_A ≥ 0.7, CR ≥ 0.8, AVE ≥ 0.50, and CA ≥ 0.80. Therefore, it is seen that all the constructs were above a threshold value and acceptable range as benchmark suggested by Nunally and Bernstein (1978) . Moreover, the values for Cronbach’s alpha was 0.936–0.953, values for AVE was 0.666–0.839, value for rho_A was 0.934–0.954, and values of CR was 0.952–0.964.

Table 1. Construct reliability and validity.

Discriminant Validity

Discriminant validity was measured using two criteria’s Fornell–Larcker and Heterotrait-Mono-Trait Ratio (HTMT). Table 2 shows the results of Fornell–Larcker criteria, as per this criterion the square root of AVE is called discriminant validity ( Fornell and Larcker, 1981 ). Therefore, it is observed in Table 2 the values were higher than the correlations was discriminant validity. Furthermore, HTMT criteria were also applied to analyze the discriminant validity. As per this criterion, the values for HTMT should be less than one ( Henseler et al., 2015 ). It is seen in Table 3 all the values of HTMT are up to the threshold value. Thus, there was no issue in discriminant validity.

Table 2. Fornell-larcker criterion.

Table 3. HTMT ratio criterion.

Structural Model

The structural model was measured through a bootstrapping test and the level of significance. The fitness of the structural model was assessed through standardized root means square residual (SRMR). According to Henseler et al. (2015) a value of a good model should have a <0.08 of SRMR value. Thus, the value for SRMR was 0.043 which below the threshold value. Moreover, the structural model explained R 2 26.5% variance in dynamic capability, 25.2% variance in entrepreneurial performance, and 30.6% variance in organizational performance. According to Chin (1998) desired values of R 2 must be greater than 0.1 or zero. Hence, the structural model results of R 2 were greater than 0.1 values which show the positive predictive significance of the model.

Testing of Hypotheses

The results of the hypotheses were shown in Table 4 and Figure 2 . This study proposed H1a KSC positively influence on DC and the results indicate that KSC has a positive and significant impact on dynamic capability (β = 0.203 ∗∗ , t = 4.567, and p < 0.001). Moreover, we predicted H1b KSC positively influence on EP and the findings illustrate that KSC positively related to the EP (β = 0.157 ∗∗ , t = 3.116, and p < 0.002). Meanwhile, we proposed H1c KSC positively effect on OP and the outcome indicates that KSC has a positive impact on OP (β = 0.225 ∗∗ , t = 4.149, and p < 0.001). Thus, H1a, H1b, and H1c were accepted.

Table 4. Path coefficients (direct effects).

Figure 2. Structural model.

Furthermore, we predicted H2a IC positively influence on DC and results explain that IC has a positive and significant influence on DC (β = 0.188 ∗∗ , t = 4.470, and p < 0.001). Moreover, we proposed that H2b IC positively affects EP and the findings indicate that IC has a positive and significant impact on EP (β = 0.228 ∗∗ , t = 5.192, and p < 0.001). Besides, we predicted H2c IC positively influence on OP and the results illustrate that IC has a positive effect on OP (β = 0.139 ∗∗ , t = 3.191, and p < 0.001). Hence, H2a, H2b, and H2c were supported.

Additionally, we assumed that H3a AC positively influences on DC and the findings indicate that AC has a positive and significant impact on DC (β = 0.237 ∗∗ , t = 4.829, and p < 0.001). Moreover, we proposed H3b AC positively effects EP and the results show that AC has a positive and significant influence on EP (β = 0.174 ∗∗ , t = 3.641, and p < 0.001). Furthermore, we predicted H3c AC positively impact on OP and findings illustrate that AC also has a positive and significant impact on OP (β = 0.116 ∗∗ , t = 2.588, and p < 0.010). Therefore, H3a, H3b, and H3c were accepted.

Lastly we, predicted H4a that DC positively effects on EP and results indicate that DC positively influence on EP (β = 0.142 ∗∗ , t = 3.020, and p < 0.003). Moreover, we proposed H5a DC positively effect on OP and findings show that DC has a positive and significant impact on OP (β = 0.165 ∗∗ , t = 3.540, and p < 0.001). Furthermore, we predicted H6 EP positively leads to OP and the outcomes explain that EP has a positive and significant influence on OP (β = 0.110 ∗∗ , t = 2.063, and p < 0.039). Thus, H4a, H5a, and H6 were also supported.

We tested the mediating effect of dynamic capability in the relationship between knowledge sharing capacity, innovative and absorptive capacity with entrepreneurial and organizational performance and results were shown in Table 5 . We proposed H4b DC mediates positively between KSC and EP and we found that DC has a positive indirect effect in the relationship between KSC and EP (β = 0.029 ∗∗ , t = 2.385, and p < 0.017). Moreover, we predicted H4c DC positively mediates between IC and EP and we found that DC has a positive indirect influence in the relationship between IC and EP (β = 0.027 ∗∗ , t = 2.398, and p < 0.017). Furthermore, we supposed H4d DC mediates the AC and EP and the results indicate that DC has a positive and significant indirect impact in the relationship between AC and EP (β = 0.034 ∗∗ , t = 0.013, and p < 0.011).

Table 5. Mediation analysis (indirect effects).

Additionally, we predicted H5b DC positively mediates the relationship between KSC and OP and we found that DC has a positive indirect influence in the relationship KSC and OP (β = 0.033 ∗∗ , t = 2.737, and p < 0.006). Besides, we proposed H5c DC mediates positively between AC and OP and findings show that DC has a positive indirect effect in the relationship between AC and OP (β = 0.039 ∗∗ , t = 2.902, and p < 0.004). Meanwhile, we proposed H5d DC positively mediates between IC and OP and we found that DC also has an indirect effect in the relationship between IC and OP (β = 0.031 ∗∗ , t = 2.507, and p < 0.012). Hence, H5b, H5c, H5d were accepted.

The Moderating Role of Opportunity Recognition

The moderating role of OR was also testified with the help of structural model results. Table 6 and Figure 3 show the moderating impact of OR in the relationship between DC with EP and OP. Moreover, we tested H7a OR to have a significant and positive moderation effect in the relationship between DC and EP. The results indicate that OR strengthening the relationship between DC and EP (β = 0.107 ∗∗ , t = 4.135, and p < 0.001). Furthermore, we predicted H7b OR in the relationship between DC and OP and the findings show that OR strengthening the positive and significant role in the relationship between DC and OP (β = 0.143 ∗∗ , t = 3.221, and p < 0.001). Therefore, H7a and H7b were accepted.

Table 6. Moderating effects.

Figure 3. Interaction of OP and DC with EP and OR.

Common Method Bias and Multicollinearity Test

Common method bias and variance inflation factor (VIF) factors (multicollinearity) were also performed. We used Harman’s test to find out the common method bias in the data. According to Harman (1976) if all the factors merged in principle rotated matrix and the initial eigenvalue explaining >50% of the variance. There is an issue of common method bias. Therefore, we performed the analysis using principle rotated matrix and the factors emerged from factor analysis and the first factor of initial eigenvalue explaining 40.24% of the total variance. Thus, there is no issue of common method bias in the data. Furthermore, the VIF test also performed. As suggested by Aiken et al. (1991) value of VIF should be between the 5 to 10 were acceptable and if the values were above 10 there is an issue in multicollinearity. The output of Table 7 shows that there is no issue of multicollinearity in the data.

Table 7. Cross loadings.

This study investigates the impact of dynamic capability as a mediator and the role of opportunity recognition as a moderator between dynamic capability with entrepreneurial and organizational performance. The study path coefficient provides empirical support to the proposed hypotheses and found significant findings with p -value < 0.05 and t -value > 2. The results support our hypothesis H1a knowledge sharing capacity predicts greater DC, which supported the explanation and consistent with the prior studies of Chirico and Nordqvist (2010) and Ferreira et al. (2020b) . The dynamic capability is helpful incompetency to figure, integrate, and reconfigure internal and external capability to enhance rapid change in the environment. The result of H1b offers that knowledge sharing capacity has a positive relationship with entrepreneurial performance and the findings are in line with the previous researchers commented on by Hsu et al. (2007) and Liao et al. (2007) . The result of H1c confirms that knowledge sharing capacity has a significant impact on organizational performance and commented with the studies of Torabi and El-Den (2017) and Ali et al. (2019) .

The result of H2a proposed that innovative capacity influenced dynamic capability and the outcome is consistent with the prior studies of Hung et al. (2010) and Ferreira et al. (2020b) . The result of H2b offers that innovative capacity has a positive impact on entrepreneurial performance and the finding is similar to a prior study of Jantunen et al. (2005) . The outcome of H2c proposed that innovative capacity positively influenced organizational performance and finding is matched with the previous study of Furman et al. (2002) .

Moreover, the finding of H3a found that absorptive capacity positively affects dynamic capability and the results are consistent with existing studies ( Chaudhary and Batra, 2018 ; Kale et al., 2019 ). Meanwhile, the result of H3b suggested that absorptive capacity positively influenced entrepreneurial performance, and finding is matched with the study of Kang and Lee (2017) . The result of H3c supported that absorptive capacity has a positive impact on organizational performance and the finding is in line with the previous researcher ( Chaudhary, 2019 ).

The finding of H4a dynamic capability has a positive influence on entrepreneurial performance. This result is consistent with the prior scholar ( Ferreira et al., 2020b ). Furthermore, the H5a result stated that dynamic capability positively and significantly related to the organizational performance, and finding is matched to the existing study of Fainshmidt et al. (2016) . Besides, the result of H6 suggested that entrepreneurial performance significantly influenced organizational performance, and the result of H4b stated that dynamic capability as a mediating effect in the relationship between knowledge sharing capacity and entrepreneurial performance. This finding is similar to previous researchers ( Hsu et al., 2007 ; Swoboda and Olejnik, 2016 ; Torabi and El-Den, 2017 ). The result of H4c confirms that innovative capacity trigger dynamic capability on entrepreneurial performance and the result is consistent with ( Hung et al., 2010 ). The result of H4d stated that dynamic capability positively mediates the relationship with absorptive capacity and entrepreneurial performance and finding is confirmed to ( Ahn et al., 2016 ).

Additionally, the result of H5b suggested that dynamic capability positively mediates in the relationship between knowledge sharing capacity and organizational performance, and the findings are consistent with prior studies of Protogerou et al. (2012) and Teece (2016) . The finding of H5c recommended that dynamic capability positively mediates in the relationship between absorptive capacity and organizational performance. This result is similar to Zhou et al. (2019) . The result of H5d found that dynamic capability positively mediates the relationship between innovative capacity and organizational performance. This finding is matched to ( Bamel and Bamel, 2018 ).

Lastly, the result of H7a found that opportunity recognition positively moderates the relationship between dynamic capability and entrepreneurial performance. The finding stated that opportunity recognition strengthens the positive and significant moderation effect on the relationship between dynamic capability and entrepreneurial performance. This output is consistent with prior studies of Sanz-Velasco (2006) and Roundy et al. (2018) . Moreover, the result of H7b suggested that opportunity recognition moderates in the relationship between dynamic capability and organizational performance. This result is also in line with the prior findings of researchers ( Jiang et al., 2018 ; Ploum et al., 2018 ).

This research extends the existing literature by exploring the importance of knowledge management practices, dynamic capabilities, and opportunity recognition to increase SME’s entrepreneurial and organizational performance. Numerous researches have been devoted to evaluating the SME’s performance and recognized the role of knowledge management practices with dynamic capabilities to achieve appropriate results. Therefore, the dynamic capabilities of SMEs in the term or knowledge management practices via capabilities and opportunities play a vital role in entrepreneurial and organizational performance. The finding of this research indicated that knowledge management practices regulate the SME’s entrepreneurial and organizational performance with the significant values of beta coefficient, t -values, and p -values. Furthermore, results suggested that dynamic capabilities play a vital role in SME’s performance, and opportunity recognition moderates the relationship between dynamic capability with entrepreneurial and organizational performance. These arguments narrate how knowledge management practices assist entrepreneurs and organizations in performance, which may positively affect on unemployment and economic growth in a country.

Practical Implications

This study has some practical implications for industry practitioners, the SME sector, and researchers in the field of entrepreneurship and organizational performance. Firstly, the study contributes to the scientific literature of SME’s performances, knowledge management capacities, dynamic capabilities, and opportunities. For a better understanding of government and non-government textile-based SME sectors, recommended deriving from this research result, which is beneficial in reducing the graph of failure business. Secondly, this study suggested that textile-based SMEs with less performance will get much assistance through this research. Thirdly, this study helps SMEs to establish a more effective way to transfer knowledge in an organization to develop a strong environment for achieving organizational goals-against competitors. It is important for the organizational operation and emerging economies because the organization faces a shortage of internal and external information, which affects the SME’s performance. Fourthly, with the help of dynamic capabilities, SMEs develop the organizational and entrepreneurial quality across the organizational boundaries. Furthermore, this study also brings riven literature on knowledge management capacities into a broader perspective for SME’s performances.

Limitations and Future Research Directions

The study has few limitations, which need to be acknowledged. The data was collected from one source or the same source. The limitation for the cross-sectional nature of data also exists, and for future research, for researchers, longitudinal data is recommended. For future research direction, this model will assist in multi-disciplinary SMEs, to raise the level of entrepreneurial and organizational performance in Pakistan. The precise and better conclusion for researchers may consider demographics, government policies, and regulation for SMEs as control variables. Here, another limitation related to the study, the sample population was bound to the gender and capture 100% of males due to the selected region. The business was based on male category businesses. This research finding may be affected due to gender discrimination. So, for future research replication to the current study should consider the gender composition. Finally, the proposed model of research was tested on Pakistani male entrepreneurs and organizations running through the male businessman. However, for future recommendation, the research may consider more and different industries, including big-size sample data with male and female entrepreneurs. This research may replicate and increase in the research model for applicability to find.

Furthermore, future researchers also conduct a similar pattern of research in a different time frame. As it is aforementioned that knowledge and innovation capacity is not constant it grew and may enhance as the context evolved with development. Hence, the knowledge and learning ability of a person may vary as time passes. It’s the main course of reason to suggest future researchers conduct a longitudinal study for the spectrum presented in this research.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

SA conceived the idea and developed a proposed model to discuss. All authors provided critical feedback and helped to shape the research, analysis, and manuscript. CL led the whole project and direct in all steps, she refined the idea and directed all authors to move on. SA and IB collectively worked to design the research plan. They encouraged FS and MR to investigate and pilot testing. Further all authors participated in data collection. MM and NS developed the theory and SA helped him to perform the computations. FS and IB verified the analytical methods. SA and MM took the lead in writing the manuscript and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript. All authors provided critical feedback and helped to shape the research, analysis, and manuscript. At the final stage and revision of the manuscript MM, NS, and MR prepared the document according to the mutually decided pattern which has considered as the best presentation of prescribed research design.

This work was supported by the self-organized cluster entrepreneurship behavior reform, evolution, and promotion strategies study (No. 16BGL028), the China National Social Science Foundation; the study on Bottleneck and Innovation of Postindustrial Intellectual Capital Development in Jiangsu Province (No. 14JD009), Jiangsu Province Social Science Foundation Project; and perception of fairness in self-organized mass Entrepreneurship (No. 4061160023).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abuaddous, H. Y., Al Sokkar, A. A., and Abualodous, B. I. (2018). The impact of knowledge management on organizational performance. Int. J. Adv. Comput. Sci. Appl. 9, 204–208.

Google Scholar

Abubakar, A. M., Elrehail, H., Alatailat, M. A., and Elçi, A. (2019). Knowledge management, decision-making style and organizational performance. J. Innov. Knowl. 4, 104–114. doi: 10.1016/j.jik.2017.07.003

CrossRef Full Text | Google Scholar

Ahn, J. M., Ju, Y., Moon, T. H., Minshall, T., Probert, D., Sohn, S. Y., et al. (2016). Beyond absorptive capacity in open innovation process: the relationships between openness, capacities and firm performance. Technol. Anal. Strateg. Manag. 28, 1009–1028. doi: 10.1080/09537325.2016.1181737

Aiken, L. S., West, S. G., and Reno, R. R. (1991). Multiple Regression: Testing and Interpreting Interactions. Thousand Oaks, CA: Sage.

Akhavan, P., Ebrahim, N. A., Fetrati, M. A., and Pezeshkan, A. (2016). Major trends in knowledge management research: a bibliometric study. Scientometrics 107, 1249–1264. doi: 10.1007/s11192-016-1938-x

Alaarj, S., Abidin-Mohamed, Z., and Bustamam, U. S. B. A. (2016). Mediating role of trust on the effects of knowledge management capabilities on organizational performance. Procedia Soc. Behav. Sci. 235, 729–738. doi: 10.1016/j.sbspro.2016.11.074

Al-Dhaafri, H. S., Al-Swidi, A. K., and Yusoff, R. Z. B. (2016). The mediating role of total quality management between the entrepreneurial orientation and the organizational performance. TQM J. 28, 89–111. doi: 10.1108/tqm-03-2014-0033

Al-Henzab, J., Tarhini, A., and Obeidat, B. Y. (2018). The associations among market orientation, technology orientation, entrepreneurial orientation and organizational performance. Benchmarking 25, 3117–3142. doi: 10.1108/bij-02-2017-0024

Ali, A. A., Paris, L., and Gunasekaran, A. (2019). Key factors influencing knowledge sharing practices and its relationship with organizational performance within the oil and gas industry. J. Knowl. Manag. 23, 1806–1837. doi: 10.1108/jkm-06-2018-0394

Ambrosini, V., and Bowman, C. (2009). What are dynamic capabilities and are they a useful construct in strategic management? Int. J. Manag. Rev. 11, 29–49. doi: 10.1111/j.1468-2370.2008.00251.x

Antunes, H. D. J. G., and Pinheiro, P. G. (2020). Linking knowledge management, organizational learning and memory. J. Innov. Knowl. 5, 140–149. doi: 10.1016/j.jik.2019.04.002

Atuahene-Gima, K. (2005). Resolving the capability–rigidity paradox in new product innovation. J. Mark. 69, 61–83. doi: 10.1509/jmkg.2005.69.4.61

PubMed Abstract | CrossRef Full Text | Google Scholar

Bagheri, A. (2017). The impact of entrepreneurial leadership on innovation work behavior and opportunity recognition in high-technology SMEs. J. High Technol. Manag. Res. 28, 159–166. doi: 10.1016/j.hitech.2017.10.003

Bamel, U. K., and Bamel, N. (2018). Organizational resources, KM process capability and strategic flexibility: a dynamic resource-capability perspective. J. Knowl. Manag. 22, 1555–1572. doi: 10.1108/jkm-10-2017-0460

Battisti, M., and Deakins, D. (2017). The relationship between dynamic capabilities, the firm’s resource base and performance in a post-disaster environment. Int. Small Bus. J. 35, 78–98. doi: 10.1177/0266242615611471

Braganza, A., Brooks, L., Nepelski, D., Ali, M., and Moro, R. (2017). Resource management in big data initiatives: processes and dynamic capabilities. J. Bus. Res. 70, 328–337. doi: 10.1016/j.jbusres.2016.08.006

Butt, M. A., Nawaz, F., Hussain, S., Sousa, M. J., Wang, M., Sumbal, M. S., et al. (2019). Individual knowledge management engagement, knowledge-worker productivity, and innovation performance in knowledge-based organizations: the implications for knowledge processes and knowledge-based systems. Comput. Math. Organ. Theory 25, 336–356. doi: 10.1007/s10588-018-9270-z

Byukusenge, E., and Munene, J. C. (2017). Knowledge management and business performance: does innovation matter? Cogent Bus. Manag. 4:1368434.

Campos, H. M. (2017). Impact of entrepreneurial passion on entrepreneurial orientation with the mediating role of entrepreneurial alertness for technology-based firms in Mexico. J. Small Bus. Enterp. Dev. 24, 353–374. doi: 10.1108/jsbed-10-2016-0166

Carmeli, A., Gelbard, R., and Reiter-Palmon, R. (2013). Leadership, creative problem-solving capacity, and creative performance: the importance of knowledge sharing. Hum. Resour. Manag. 52, 95–121. doi: 10.1002/hrm.21514

Centobelli, P., Cerchione, R., and Esposito, E. (2019). Efficiency and effectiveness of knowledge management systems in SMEs. Prod. Plan. Control 30, 779–791. doi: 10.1080/09537287.2019.1582818

Cerchione, R., and Esposito, E. (2016). A systematic review of supply chain knowledge management research: state of the art and research opportunities. Int. J. Prod. Econ. 182, 276–292. doi: 10.1016/j.ijpe.2016.09.006

Chaudhary, S. (2019). Implications of strategic flexibility in small firms: the moderating role of absorptive capacity. South Asian J. Bus. Stud. 8, 370–386. doi: 10.1108/sajbs-10-2018-0104

Chaudhary, S., and Batra, S. (2018). Absorptive capacity and small family firm performance: exploring the mediation processes. J. Knowl. Manag. 22, 1201–1216. doi: 10.1108/jkm-01-2017-0047

Chavez, R., Yu, W., Jacobs, M. A., and Feng, M. (2017). Manufacturing capability and organizational performance: the role of entrepreneurial orientation. Int. J. Prod. Econ. 184, 33–46. doi: 10.1016/j.ijpe.2016.10.028

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 295, 295–336.

Chirico, F., and Nordqvist, M. (2010). Dynamic capabilities and trans-generational value creation in family firms: the role of organizational culture. Int. Small Bus. J. 28, 487–504. doi: 10.1177/0266242610370402

Colbert, A. E., Kristof-Brown, A. L., Bradley, B. H., and Barrick, M. R. (2008). CEO transformational leadership: the role of goal importance congruence in top management teams. Acad. Manag. J. 51, 81–96. doi: 10.5465/amj.2008.30717744

Davenport, T. H., and Prusak, L. (1998). Working Knowledge: How Organizations Manage what they Know. Brighton, MA: Harvard Business Publishing.

Fainshmidt, S., Pezeshkan, A., Lance Frazier, M., Nair, A., and Markowski, E. (2016). Dynamic capabilities and organizational performance: a meta-analytic evaluation and extension. J. Manag. Stud. 53, 1348–1380. doi: 10.1111/joms.12213

Falasca, M., Zhang, J., Conchar, M., and Li, L. (2017). The impact of customer knowledge and marketing dynamic capability on innovation performance: an empirical analysis. J. Bus. Ind. Mark. 32, 901–912. doi: 10.1108/jbim-12-2016-0289

Ferreira, J., Cardim, S., and Coelho, A. (2020a). Dynamic capabilities and mediating effects of innovation on the competitive advantage and firm’s performance: the moderating role of organizational learning capability. J. Knowl. Econ. 10, 1–25.

Ferreira, J., Coelho, A., and Moutinho, L. (2020b). Dynamic capabilities, creativity and innovation capability and their impact on competitive advantage and firm performance: the moderating role of entrepreneurial orientation. Technovation 92:102061. doi: 10.1016/j.technovation.2018.11.004

Filser, M., and Eggers, F. (2014). Entrepreneurial orientation and performance of firms: a comparative study of Australia, Liechtenstein and Switzerland. S. Afr. J. Bus. Manag. 45, 56–62.

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50. doi: 10.1177/002224378101800104

Furman, J. L., Porter, M. E., and Stern, S. (2002). The determinants of national innovative capacity. Res. Policy 31, 899–933. doi: 10.1016/s0048-7333(01)00152-4

García-Morales, V. J., Lloréns-Montes, F. J., and Verdú-Jover, A. J. (2008). The effects of transformational leadership on organizational performance through knowledge and innovation. Br. J. Manag. 19, 299–319. doi: 10.1111/j.1467-8551.2007.00547.x

García-Sánchez, E., García-Morales, V. J., and Martín-Rojas, R. (2018). Influence of technological assets on organizational performance through absorptive capacity, organizational innovation and internal labour flexibility. Sustainability 10:770. doi: 10.3390/su10030770

George, N. M., Parida, V., Lahti, T., and Wincent, J. (2016). A systematic literature review of entrepreneurial opportunity recognition: insights on influencing factors. Int. Entrep. Manag. J. 12, 309–350. doi: 10.1007/s11365-014-0347-y

Gholami, M. H., Asli, M. N., Nazari-Shirkouhi, S., and Noruzy, A. (2013). Investigating the influence of knowledge management practices on organizational performance: an empirical study. Acta Polytech. Hung. 10, 205–216.

Gieske, H., van Buuren, A., and Bekkers, V. (2016). Conceptualizing public innovative capacity: a framework for assessment. Innov. J. 21:1.

Ha, S. T., and Lo, M. C. (2018). An empirical examination of knowledge management and organisational performance among Malaysian manufacturing SMEs. Int. J. Bus. Innov. Res. 17, 23–37. doi: 10.1504/ijbir.2018.10015131

Hair, J., Black, W., Babin, B., and Anderson, R. (2010). Multivariate Data Analysis: Global Edition. Upper Saddle River, NJ: Pearson.

Hair, J. F., Ringle, C. M., and Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. J. Mark. Theory Pract. 19, 139–152.

Halkos, G., and Skouloudis, A. (2018). Corporate social responsibility and innovative capacity: intersection in a macro-level perspective. J. Clean. Prod. 182, 291–300. doi: 10.1016/j.jclepro.2018.02.022

Harman, H. H. (1976). Modern Factor Analysis. Chicago, IL: University of Chicago Press.

Hartono, H., and Halim, E. (2014). The impact of knowledge management and entrepreneur’s knowledge on innovation and firm performance. Winners 15, 108–114. doi: 10.21512/tw.v15i2.624

Hasan, F. S., Almubarak, M. M. S., and Ahmed, A. (2016). Factors influencing women entrepreneurs’ performance in SMEs. World J. Entrep. Manag. Sustain. Dev. 12, 82–101. doi: 10.1108/wjemsd-09-2015-0037

Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43, 115–135. doi: 10.1007/s11747-014-0403-8

Hernández-Perlines, F., Ariza-Montes, A., Han, H., and Law, R. (2019). Innovative capacity, quality certification and performance in the hotel sector. Int. J. Hosp. Manag. 82, 220–230. doi: 10.1016/j.ijhm.2019.04.027

Hmieleski, K. M., and Baron, R. A. (2008). Regulatory focus and new venture performance: a study of entrepreneurial opportunity exploitation under conditions of risk versus uncertainty. Strateg. Entrep. J. 2, 285–299. doi: 10.1002/sej.56

Hsu, M.-H., Ju, T. L., Yen, C.-H., and Chang, C.-M. (2007). Knowledge sharing behavior in virtual communities: the relationship between trust, self-efficacy, and outcome expectations. Int. J. Hum. Comput. Stud. 65, 153–169. doi: 10.1016/j.ijhcs.2006.09.003

Hu, R., Wang, L., Zhang, W., and Bin, P. (2018). Creativity, proactive personality, and entrepreneurial intention: the role of entrepreneurial alertness. Front. Psychol. 9:951. doi: 10.3389/fpsyg.2018.00951

Hung, R. Y. Y., Yang, B., Lien, B. Y.-H., McLean, G. N., and Kuo, Y.-M. (2010). Dynamic capability: impact of process alignment and organizational learning culture on performance. J. World Bus. 45, 285–294. doi: 10.1016/j.jwb.2009.09.003

Hurley, R. F., and Hult, G. T. M. (1998). Innovation, market orientation, and organizational learning: an integration and empirical examination. J. Mark. 62, 42–54. doi: 10.1177/002224299806200303

Ince, H., Imamoglu, S. Z., and Turkcan, H. (2016). The effect of technological innovation capabilities and absorptive capacity on firm innovativeness: a conceptual framework. Procedia Soc. Behav. Sci. 235, 764–770. doi: 10.1016/j.sbspro.2016.11.078

Jantunen, A., Puumalainen, K., Saarenketo, S., and Kyläheiko, K. (2005). Entrepreneurial orientation, dynamic capabilities and international performance. J. Int. Entrep. 3, 223–243. doi: 10.1007/s10843-005-1133-2

Jiang, W., Chai, H., Shao, J., and Feng, T. (2018). Green entrepreneurial orientation for enhancing firm performance: a dynamic capability perspective. J. Clean. Prod. 198, 1311–1323. doi: 10.1016/j.jclepro.2018.07.104

Kale, E., Aknar, A., and Başar, Ö. (2019). Absorptive capacity and firm performance: the mediating role of strategic agility. Int. J. Hosp. Manag. 78, 276–283. doi: 10.1016/j.ijhm.2018.09.010

Kane, G. C. (2017). The evolutionary implications of social media for organizational knowledge management. Inf. Organ. 27, 37–46. doi: 10.1016/j.infoandorg.2017.01.001

Kang, M., and Lee, M.-J. (2017). Absorptive capacity, knowledge sharing, and innovative behaviour of R&D employees. Technol. Anal. Strateg. Manag. 29, 219–232. doi: 10.1080/09537325.2016.1211265

Kantur, D. (2016). Strategic entrepreneurship: mediating the entrepreneurial orientation-performance link. Manag. Decis. 54, 24–43. doi: 10.1108/md-11-2014-0660

Keikha, F. (2018). Interpersonal trust factors affecting members’ knowledge sharing behavior in virtual communities. Iran. J. Inf. Process. Manag. 34, 275–300.

Kim, J. Y., Choi, D. S., Sung, C.-S., and Park, J. Y. (2018). The role of problem solving ability on innovative behavior and opportunity recognition in university students. J. Open Innov. Technol. Mark. Complex. 4:4.

Kuckertz, A., Kollmann, T., Krell, P., and Stöckmann, C. (2017). Understanding, differentiating, and measuring opportunity recognition and opportunity exploitation. Int. J. Entrep. Behav. Res. 23, 78–97. doi: 10.1108/ijebr-12-2015-0290

Latukha, M., and Veselova, A. (2019). Talent management, absorptive capacity, and firm performance: does it work in China and Russia? Hum. Resour. Manag. 58, 503–519. doi: 10.1002/hrm.21930

Lawson, C., and Lorenz, E. (1999). Collective learning, tacit knowledge and regional innovative capacity. Reg. Stud. 33, 305–317. doi: 10.1080/713693555

Leal-Rodríguez, A. L., Roldán, J. L., Ariza-Montes, J. A., and Leal-Millán, A. (2014). From potential absorptive capacity to innovation outcomes in project teams: the conditional mediating role of the realized absorptive capacity in a relational learning context. Int. J. Proj. Manag. 32, 894–907. doi: 10.1016/j.ijproman.2014.01.005

Li, C., Murad, M., Shahzad, F., Khan, M. A. S., and Ashraf, S. F. (2020a). Dark tetrad personality traits and counterproductive work behavior among doctors in Pakistan. Int. J. Health Plan. Manag. 35, 1173–1192. doi: 10.1002/hpm.3025

Li, C., Murad, M., Shahzad, F., Khan, M. A. S., Ashraf, S. F., and Dogbe, C. S. K. (2020b). Entrepreneurial passion to entrepreneurial behavior: role of entrepreneurial alertness, entrepreneurial self-efficacy and proactive personality. Front. Psychol. 11:1611. doi: 10.3389/fpsyg.2020.01611

Liao, S.-H., Fei, W.-C., and Chen, C.-C. (2007). Knowledge sharing, absorptive capacity, and innovation capability: an empirical study of Taiwan’s knowledge-intensive industries. J. Inf. Sci. 33, 340–359. doi: 10.1177/0165551506070739

Likoum, S. W. B., Shamout, M. D., Harazneh, I., and Abubakar, A. M. (2020). Market-sensing capability, innovativeness, brand management systems, market dynamism, competitive intensity, and performance: an integrative review. J. Knowl. Econ. 11, 593–613. doi: 10.1007/s13132-018-0561-x

Lin, Y., and Wu, L.-Y. (2014). Exploring the role of dynamic capabilities in firm performance under the resource-based view framework. J. Bus. Res. 67, 407–413. doi: 10.1016/j.jbusres.2012.12.019

Liu, C.-H., Horng, J.-S., Chou, S.-F., Huang, Y.-C., and Chang, A. Y. (2018). How to create competitive advantage: the moderate role of organizational learning as a link between shared value, dynamic capability, differential strategy, and social capital. Asia Pac. J. Tour. Res. 23, 747–764. doi: 10.1080/10941665.2018.1492943

Liu, F., Dutta, D. K., and Park, K. (2020). From external knowledge to competitive advantage: absorptive capacity, firm performance, and the mediating role of labour productivity. Technol. Anal. Strateg. Manag. 1–13. doi: 10.1080/09537325.2020.1787373

Lopes, C. M., Scavarda, A., Hofmeister, L. F., Thomé, A. M. T., and Vaccaro, G. L. R. (2017). An analysis of the interplay between organizational sustainability, knowledge management, and open innovation. J. Clean. Prod. 142, 476–488. doi: 10.1016/j.jclepro.2016.10.083

Mardani, A., Nikoosokhan, S., Moradi, M., and Doustar, M. (2018). The relationship between knowledge management and innovation performance. J. High Technol. Manag. Res. 29, 12–26.

Miao, C., Qian, S., and Ma, D. (2017). The relationship between entrepreneurial self-efficacy and firm performance: a meta-analysis of main and moderator effects. J. Small Bus. Manag. 55, 87–107. doi: 10.1111/jsbm.12240

Najmi, K., Kadir, A. R., and Kadir, M. I. A. (2018). Mediation effect of dynamic capability in the relationship between knowledge management and strategic leadership on organizational performance accountability. Int. J. Law Manag. 60, 517–529. doi: 10.1108/ijlma-01-2017-0004

Neneh, B. N. (2019). From entrepreneurial alertness to entrepreneurial behavior: the role of trait competitiveness and proactive personality. Pers. Individ. Dif. 138, 273–279. doi: 10.1016/j.paid.2018.10.020

Ngah, R., Tai, T., and Bontis, N. (2016). Knowledge management capabilities and organizational performance in roads and transport authority of Dubai: the mediating role of learning organization. Knowl. Process Manag. 23, 184–193. doi: 10.1002/kpm.1504

Nunally, J. C., and Bernstein, I. H. (1978). Psychometric Theory. New York, NY: McGraw-Hill.

Obschonka, M., and Hahn, E. (2018). Personal agency in newly arrived refugees: the role of personality, entrepreneurial cognitions and intentions, and career adaptability. J. Vocat. Behav. 105, 173–184. doi: 10.1016/j.jvb.2018.01.003

Ohemeng, F. L., and Kamga, O. (2020). Administrative leaders as institutional entrepreneurs in developing countries: a study of the development and institutionalization of performance management in Ghana’s public service. Public Adm. Dev. 40, 87–100. doi: 10.1002/pad.1867

Pezeshkan, A., Fainshmidt, S., Nair, A., Frazier, M. L., and Markowski, E. (2016). An empirical assessment of the dynamic capabilities–performance relationship. J. Bus. Res. 69, 2950–2956. doi: 10.1016/j.jbusres.2015.10.152

Ploum, L., Blok, V., Lans, T., and Omta, O. (2018). Exploring the relation between individual moral antecedents and entrepreneurial opportunity recognition for sustainable development. J. Clean. Prod. 172, 1582–1591. doi: 10.1016/j.jclepro.2017.10.296

Podsakoff, P. M., MacKenzie, S. B., and Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annu. Rev. Psychol. 63, 539–569. doi: 10.1146/annurev-psych-120710-100452

Proksch, D., Haberstroh, M. M., and Pinkwart, A. (2017). Increasing the national innovative capacity: identifying the pathways to success using a comparative method. Technol. Forecast. Soc. Change 116, 256–270. doi: 10.1016/j.techfore.2016.10.009

Protogerou, A., Caloghirou, Y., and Lioukas, S. (2012). Dynamic capabilities and their indirect impact on firm performance. Ind. Corp. Change 21, 615–647. doi: 10.1093/icc/dtr049

Rafique, M., Hameed, S., and Agha, M. H. (2018). Impact of knowledge sharing, learning adaptability and organizational commitment on absorptive capacity in pharmaceutical firms based in Pakistan. J. Knowl. Manag. 22, 44–56. doi: 10.1108/jkm-04-2017-0132

Rangus, K., and Slavec, A. (2017). The interplay of decentralization, employee involvement and absorptive capacity on firms’ innovation and business performance. Technol. Forecast. Soc. Change 120, 195–203. doi: 10.1016/j.techfore.2016.12.017

Rašula, J., Vukšić, V. B., and Štemberger, M. I. (2012). The impact of knowledge management on organisational performance. Econ. Bus. Rev. 14:22.

Raza, S., Minai, M. S., ul Haq, M. A., Ismail, A. I., and Zain, A. Y. M. (2018). Entrepreneurial network towards small firm performance through dynamic capabilities: the conceptual perspective. Acad. Entrep. J. 24, 1–9.

Roundy, P. T., Harrison, D. A., Khavul, S., Pérez-Nordtvedt, L., and McGee, J. E. (2018). Entrepreneurial alertness as a pathway to strategic decisions and organizational performance. Strateg. Organ. 16, 192–226. doi: 10.1177/1476127017693970

Santoro, G., Vrontis, D., Thrassou, A., and Dezi, L. (2018). The internet of things: building a knowledge management system for open innovation and knowledge management capacity. Technol. Forecast. Soc. Change 136, 347–354. doi: 10.1016/j.techfore.2017.02.034

Sanz-Velasco, S. A. (2006). Opportunity development as a learning process for entrepreneurs. Int. J. Entrep. Behav. 12, 251–271. doi: 10.1108/13552550610687637

Serrat, O. (2017). Knowledge Solutions: Tools, Methods, and Approaches to Drive Organizational Performance. London: Springer Nature.

Shujahat, M., Sousa, M. J., Hussain, S., Nawaz, F., Wang, M., and Umer, M. (2019). Translating the impact of knowledge management processes into knowledge-based innovation: the neglected and mediating role of knowledge-worker productivity. J. Bus. Res. 94, 442–450. doi: 10.1016/j.jbusres.2017.11.001

Swoboda, B., and Olejnik, E. (2016). Linking processes and dynamic capabilities of international SMEs: the mediating effect of international entrepreneurial orientation. J. Small Bus. Manag. 54, 139–161. doi: 10.1111/jsbm.12135

Tang, H. (2017). Effects of leadership behavior on knowledge management and organization innovation in medicine and health sciences. Eurasia J. Math. Sci. Technol. Educ. 13, 5425–5433.

Teece, D., Peteraf, M., and Leih, S. (2016). Dynamic capabilities and organizational agility: risk, uncertainty, and strategy in the innovation economy. Calif. Manag. Rev. 58, 13–35. doi: 10.1525/cmr.2016.58.4.13

Teece, D. J. (2016). Dynamic capabilities and entrepreneurial management in large organizations: toward a theory of the (entrepreneurial) firm. Eur. Econ. Rev. 86, 202–216. doi: 10.1016/j.euroecorev.2015.11.006

Torabi, F., and El-Den, J. (2017). The impact of knowledge management on organizational productivity: a case study on Koosar Bank of Iran. Procedia Comput. Sci. 124, 300–310. doi: 10.1016/j.procs.2017.12.159

Verma, V., Bharadwaj, S. S., and Nanda, M. (2017). Comparing agility and absorptive capacity for superior firm performance in dynamic environment. Int. J. Bus. Environ. 9, 1–17. doi: 10.1504/ijbe.2017.10005662

Wahda, W. (2017). Mediating effect of knowledge management on organizational learning culture toward organization performance. J. Manag. Dev. 36, 846–858. doi: 10.1108/jmd-11-2016-0252

Wang, Z., and Kim, H. G. (2017). Can social media marketing improve customer relationship capabilities and firm performance? Dynamic capability perspective. J. Interact. Mark. 39, 15–26. doi: 10.1016/j.intmar.2017.02.004

Wu, J., Ma, Z., and Zhuo, S. (2017). Enhancing national innovative capacity: the impact of high-tech international trade and inward foreign direct investment. Int. Bus. Rev. 26, 502–514. doi: 10.1016/j.ibusrev.2016.11.001

Xing, X., Liu, T., Shen, L., and Wang, J. (2020). Linking environmental regulation and financial performance: the mediating role of green dynamic capability and sustainable innovation. Sustainability 12:1007. doi: 10.3390/su12031007

Xue, M., Boadu, F., and Xie, Y. (2019). The penetration of green innovation on firm performance: effects of absorptive capacity and managerial environmental concern. Sustainability 11:2455. doi: 10.3390/su11092455

Zaim, H., Muhammed, S., and Tarim, M. (2019). Relationship between knowledge management processes and performance: critical role of knowledge utilization in organizations. Knowl. Manag. Res. Pract. 17, 24–38. doi: 10.1080/14778238.2018.1538669

Zhou, S. S., Zhou, A. J., Feng, J., and Jiang, S. (2019). Dynamic capabilities and organizational performance: the mediating role of innovation. J. Manag. Organ. 25, 731–747. doi: 10.1017/jmo.2017.20

Keywords : knowledge management practices, dynamic capability, opportunity recognition, organizational performance, entrepreneurial performance, mediated-moderated model

Citation: Li C, Ashraf SF, Shahzad F, Bashir I, Murad M, Syed N and Riaz M (2020) Influence of Knowledge Management Practices on Entrepreneurial and Organizational Performance: A Mediated-Moderation Model. Front. Psychol. 11:577106. doi: 10.3389/fpsyg.2020.577106

Received: 28 June 2020; Accepted: 29 September 2020; Published: 03 December 2020.

Reviewed by:

Copyright © 2020 Li, Ashraf, Shahzad, Bashir, Murad, Syed and Riaz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sheikh Farhan Ashraf, [email protected] ; Majid Murad, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Med J Islam Repub Iran

The current understanding of knowledge management concepts: A critical review

Shahram yazdani.

1 Virtual school of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Snor Bayazidi

Amir ali mafi.

2 Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Background: Higher education institutions include experts who are knowledgeable. Knowledge management facilitates institutions to enhance the capacity to collect information and knowledge and apply it to problem-solving and decision making. Through the review of related studies, we observed that there are multiple concepts and terms in the field of knowledge management. Thus, the complexity and variety of these concepts and definitions must be clarified. Considering the importance of clarifying these concepts for utilization by users, this study aimed to examine the concepts related to this filed.

Methods: The methodology used in this study was based on the Carnwell and Daly's critical review method. An extensive search was carried out on various databases and libraries. A critical and profound review was carried out on selected articles. Many wandering concepts were found. Identified concepts were classified into seven categories based on conceptual proximity. Existing definitions and evidence in relation to extracted concepts were criticized and synthesized. The definitional attributes for them were identified and a conceptual identity card was provided for each of the concepts.

Results: Thirty-seven concepts with the most relevance to the field of knowledge management were extracted. There was no clear boundary among them, and they wandered. To avoid more confusion, concepts were classified according to semantic relation. Eight categories were created; each category consisted of a mother concept and several other concepts with similarity and proximity to the meaning of the original concept. Their attributes have been identified, and finally, each of them was presented in the form of a conceptual identity card.

Conclusion: Through critically reviewing the literature in this field, we were able to identify the concepts and realize their attributes. In this way, we came to a new interpretation of the concepts. At the end of the study, we concluded that some of the concepts have not been properly defined and are not properly located in the knowledge management field; also their application is uncertain.

↑ What is “already known” in this topic:

There are numerous and complex concepts in the field of knowledge management that have not been clarified, and most of them are used incorrectly. For example, in many studies, the concept of knowledge management and knowledge translation are used interchangeably, and there is no distinct boundary among concepts.

→ What this article adds:

The identified concepts were wandering. To avoid more confusion, concepts were classified according to semantic relation. Eight categories were created, including a sentinel concept and several other neighbor concepts. Their attributes have been identified, and finally, each of them was presented in the form of a conceptual identity card.

Introduction

The organization in the age of knowledge is an organization that is based on the best available knowledge and information. To succeed in today's challenging organizational environment, organizations need to learn from past mistakes rather than repeating those mistakes. This process occurs through knowledge management ( 1 , 2 ). Knowledge management (KM) is important, especially for organizations that their successes depend on the production, use, and integration of knowledge by professionals and employees. Higher education institutions are made up of experts who are knowledgeable. KM is a new field in the academic environment, and many universities are actively involved in related activities in this field ( 3 ). Conferences and seminars are taking place at the national and international levels in this regard. In the field of education, due to the need to explore the power and intellectual capital available to share experiences, this area has been very much considered ( 4 ). All knowledge production organizations such as research, development centers and higher education institutions from colleges to universities are looking for new concepts in their favorite subject. They also help create knowledge through various programs, considered as "knowledge houses" ( 5 ). So, the knowledge of the professors flows to the students and new knowledge is produced. Information is created in various forms and sources such as books, articles, dissertations, reports, and more. Knowledge management helps these institutions to enhance the capacity to collect information and knowledge and apply it to problem-solving and decision making ( 6 ). Therefore, evidence shows that any academic institution is associated with knowledge. In these institutions, the information and knowledge gained in the scientific community's core area should be disseminated for further growth ( 5 ). But, there are challenges in this direction. Studies have demonstrated that knowledge created in educational institutions is not properly stored and obtained. Most of the time, knowledge created in that system remains unknown and is considered as gray literature ( 6 ). The academic environment is considered as the knowledge houses, but if the generated knowledge in that organization is not properly organized, it will minimize its usefulness and leads to repeat activities ( 7 ). Despite the importance of knowledge management for educational systems, there is still no awareness about its development by academics. There is a need to create a culture of sharing knowledge among professors, staff and students who are still afraid of losing their knowledge through exchange and dissemination ( 8 ). The use of information communication technology and the development of advanced skills in the training of professions for the purpose of participation, communication, acquisition, recording and dissemination of knowledge are used very poorly in universities. Therefore, they need to adopt a policy in this regard ( 7 ). New educational systems are market-oriented and are becoming entrepreneurs. They should be accountable to the academic governance system. Therefore, educational institutions and academics faced with global pressures, research, and interdisciplinary subjects. In the complexity of such as global education market, there is a need for a motivating environment ( 6 ).

We mentioned the importance of knowledge management in the educational system, as well as the existence of challenges in this direction, but although much research has been done in this regard, knowledge workers, those who are willing to do research or scientific activity in this area face difficulties. The main reason for this problem is that there are numerous and complex concepts in this area that have not been clarified, and most of them are used incorrectly. For example, in many studies and even by academics, the concept of knowledge management and knowledge translation are used interchangeably, and there is no distinct boundary among concepts. On the other hand, despite the multiplicity of concepts in this field, the research that has examined all of these concepts together has not been found. Considering the importance of clarifying these concepts for utilization by users, the first step in this direction is to identify and clarify concepts associated with knowledge management. Therefore, in this study, we intend to examine the concepts and definitions related to them through a critical review method, accordingly identify their attributes, and based on the identified attributes, concepts become clear.

The result of this study can help managers, policymakers, professors, students, and researchers who after us, intend to carry out research related to the field of knowledge management.

Our methodology was based on the critical review of the literature introduced by Carnwell and Daly. The following five steps were performed; 1- detremination the scope of the review, 2- identification relevant information resources, 3-literature review, 4-writing the review,5- application of the review results in the study ( 9 ).

The review scoop was theoretical research published in the research journals. An extensive search was carried out on various databases (google scholar, PubMed, Embase, Elsevier, Scopus, Iran Medex, SID, and online libraries and dictionaries). The main keywords in the search were: knowledge management concepts, knowledge management stages, knowledge management implementation, knowledge management in higher education, and knowledge management in medical education. As a result, numerous articles were found. To restrict the search results, we set the inclusion criteria and exclusion criteria. Inclusion criteria were the studies and books related to knowledge management concepts without time limitation. Non-academic research was the exclusion criteria. The articles were examined superficially. Then the primary screening was done on the titles. So, a summary of the articles was studied and those articles that were most closely related to the concepts of knowledge management were selected to study the full text. Priority in reading was based on their relevance to study objectives and literature with more conceptual richness. A critical review was carried on publications with the purpose of clarifying the boundary among concepts. Thirty-seven concepts that were involved in the KM process were extracted. Since there were many wandering concepts in this path, in order to avoid confusion, they were examined based on semantic proximity in separate categories. Each category included a mother concept and other related concepts to it. Then by synthesizing existing definitions and evidence about each of the concepts, we tried to identify the characteristics on which they are defined. Ultimately each of the concepts was presented in the new classification based on these characteristics.

There were many wandering concepts in the field of knowledge management, in order to avoid bewilderment; concepts were examined based on semantic proximity in separate categories. Each category included a mother concept with related concepts to it. By critique and comparing the definitions and evidence about each of the concepts, their attributes were identified. Finally, based on these features, a conceptual identity card for each concept was presented. Our result presented in nine categories: knowledge Generation (knowledge acquisition, knowledge selection, knowledge building, knowledge creation, knowledge capture), Knowledge processing( knowledge synthesis, knowledge integration, knowledge refinement, knowledge tailoring, knowledge customization)knowledge storage (knowledge assimilation, knowledge package, knowledge documentation, knowledge indexing), Knowledge transfer( knowledge sharing, knowledge exchange, knowledge dissemination, knowledge publication), Knowledge capitalization( knowledge commercialization, knowledge valorization), Knowledge brokering, Knowledge utilization(, knowledge adoption, knowledge adaptation, knowledge reuse), Knowledge translation, and Knowledge management.

In the following, the conceptual identity of each of the concepts, which includes the specific features about that concept, is introduced.

Knowledge Generation : Knowledge acquisition, knowledge capture, knowledge selection, knowledge creation, knowledge building.

Knowledge acquisition attributes

Purpose: The purpose of knowledge acquisition is to enhance the organizations' competitive edge through increasing an organization’s operational knowledge base ( 10 ).

Source of obtaining knowledge: The source of obtaining knowledge is internal and external sources ( 10 ).

Type of acquired Knowledge: Type of acquired knowledge can be either tacit or explicit ( 10 ).

Activities: Activities related to knowledge acquisition are identification of knowledge, obtaining the identified knowledge, transferring the knowledge for immediately using or internalization ( 11 ).

Key point: Knowledge acquired can either be tacit or explicit but must add value to the organization ( 10 ).

Knowledge selection attributes

Purpose: The purpose of knowledge selection is Identification the knowledge according to organizational needs in internal sources, Provide knowledge at the appropriate place and by the appropriate form ( 12 ).

Source of obtaining knowledge: Knowledge is obtained from internal sources ( 12 ).

Activities: Knowledge selection activities include the following: identification of knowledge from internal sources, obtaining the identified knowledge from internal sources, transfer the knowledge for immediately using or internalization ( 12 )

Key point: Knowledge selection is the opposite point of knowledge acquisition ( 12 ).

Knowledge capturing attributes

Purpose: The purpose of knowledge capture is to maintain knowledge in order to organizational performance improvement, ensure that knowledge available is stored for future reference ( 13 ).

Form: Knowledge captured in the form of databases or manuals ( 13 ).

Knowledge creation attributes

Context: Knowledge creation occurs through the inference or discovery from knowledge sources ( 12 ).

Purpose: Creating or producing knowledge helps organizations gain a competitive advantage by providing valuable, rare, and inimitable resources ( 14 ). Utilization of complex and discontinuous events and phenomena to Confronting recognized organizational problems ( 15 ).

Activities: Knowledge selection activities include the following: control the organizational knowledge, Control the external environment, Creation knowledge from the existing basic knowledge, Transfer created knowledge for externalization or internalization ( 12 ).

Knowledge creation place: Knowledge is produced in the Research community, Professional Councils, Ministries and governmental organizational, Transfer and innovation centers, Science communities ( 16 ).

Form: Some scientists have defined knowledge creation as a process, output, and outcome ( 15 , 17 ).

Knowl edge building attributes

Context: The term knowledge building first appeared in the learning sciences literature ( 18 ).

Purpose: The purpose of knowledge creation is the creation or modification of public knowledge—knowledge that lives ‘in the world’ and is available to be worked on and used by other people. These pursuits should advance the current understanding of individuals within a group, at a level beyond their initial knowledge level, and should be directed towards advancing the understanding of what is known about that topic or idea ( 19 ).

Steps: Knowledge building consists of the following steps: creation, testing, and improvement of conceptual artifacts ( 19 ).

Requirements: It encompasses the foundational learning, sub-skills, and socio-cognitive dynamics pursued in other approaches, along with the additional benefit of movement along the trajectory to mature education ( 20 ).

Path: Knowledge building can be considered as deep constructivism that involves making a collective inquiry into a specific topic and coming to a deeper understanding through interactive questioning, dialogue, and continuous improvement of ideas. Ideas are thus the medium of operation in KB environments ( 20 ).

Key point: Knowledge building projects focus on understanding rather than on accomplishing tasks, and on collaboration rather than on controversy ( 20 ).

Knowledge processing : Knowledge filtering, knowledge synthesis, knowledge integration, knowledge refinement, knowledge customization.

Knowledge processing attributes

Context: Knowledge processing is a significant factor contributing to socioeconomic sustainability ( 21 ).It is a central problem of Artificial Intelligence ( 22 ).

Purpose: The purpose of Knowledge processing is to understand the relationship among data, information and knowledge and create knowledge structures ( 23 ).

Method: The knowledge processing method is Transformation of data into knowledge, changing the form of knowledge representation, deriving new knowledge from a given knowledge ( 23 ).

Steps: Knowledge processing consists of the following steps: Information storing, information retrieving, and information transferring ( 21 ).

Key point: Knowledge processing is known as the most important factor affecting economic and social sustainability, Derive value from knowledge processing ( 23 ).

Knowledge filtering attributes

Context: Knowledge filtering can be used to facilitate assimilation. Filtering tries to get the right knowledge to the right person at the right time) 24).

Purpose: Filtering is a tool to help people find the most valuable information so that the limited time spent on reading/listening/viewing can be spent on the most interesting and valuable documents. Filters are also used to organize and structure information ( 25 ).

Steps: Knowledge filtering consists of the following steps: Evaluate documents, and puts documents, which are interesting into its structured information database) 25).

Method: The knowledge filtering method is Manual filtering by people, using intelligent agents ( 24 ).

Main actors: Computer-based Approaches, publishers, editors, journalists ( 25 ).

Knowledge synthesis attributes

Context: Knowledge synthesis is the contextualization and integration of research findings of individual research studies within the larger body of knowledge on the topic ( 26 ).

Purpose: Most syntheses are conducted either for the purpose of knowledge support or for decision support ( 27 ).

Steps: Knowledge synthesis consists of the following steps: Stating the objectives of the research, Defining eligibility criteria for studies to be included, Identifying (all) potentially eligible studies, Applying eligibility criteria, Assembling the complete data set feasible including data extraction, quality appraisal of included studies, Analyzing this data set, and Preparing a structured report ( 28 , 29 ).

Method: Knowledge synthesis methods are Systematic review, Realist syntheses, Narrative syntheses, Meta-analyses, Meta-syntheses, Practice guidelines, Consensus conference, or expert panel ( 30 ).

Key point: A synthesis must be reproducible and transparent in its methods ( 26 ).

Knowledge integration attributes

Context: The integration of knowledge is the process of incorporating new information into a body of existing knowledge ( 31 ).

Purpose: The purpose of knowledge integration is to determine how new and existing knowledge interacts and how existing knowledge should be modified to accommodate the new information ( 31 ).

Steps: Knowledge integration consists of the following steps: Dynamic process of linking, connecting, distinguishing, organizing, and structuring ideas about scientific phenomena ( 32 ).

Knowledge refinement attributes

Context: The knowledge refinement process is implemented as part of an organization’s knowledge management efforts ( 33 ).

Purpose: The purpose of knowledge refinement is to optimize content quality ( 33 , 34 ).

Steps: Knowledge refinement refers to the process of evaluating, analyzing and optimizing the knowledge object to be stored in a repository ( 35 , 36 )

Key point: Knowledge refinement effectiveness is defined as the degree to which the refinement process produces quality knowledge ( 37 ). Knowledge refinement process should positively enhance the quality of refined knowledge ( 37 ).

Knowledge customization attributes

Context: Product customization is becoming an increasingly important strategic initiative in knowledge management. Product customization impacts the knowledge management processes of knowledge acquisition, sharing, and transfer ( 38 ).

Purpose: The purpose of customization is configuring a product or service to a buyer’s specifications ( 39 ). The relationships among sales, R&D, and production functions have to strengthen and the KM system has to support such a need ( 38 ).

Steps: Knowledge customization consists of the following steps: Collecting information about the customer, choosing options and/or creating new content, deliberately tailors content ( 40 ).

Key point: Customization emphasizes the user’s role in specifying content; customization is a highly user-driven process of tailoring ( 41 ).

Knowl edge transfer attributes

Context: The transfer of knowledge in the broadest sense refers to the flow of knowledge between and within organizations ( 42 ).

Purpose: The purpose of knowledge transfer is: decision-making, changing individual or organizational behavior, developing policies, problem-solving ( 43 ).

Perspectives about Knowledge Transfer: Health perspective, educational perspective, management perspective.

Health perspective: Use of scientific research findings to improve professional performance ( 44 ).

Educational perspective: Using generated knowledge in a specific context for another context ( 45 ).

Management perspective: utilization of the new knowledge for organizational behaviors ( 46 ).

Form: Knowledge transfer can be done in the form of formal and informal, planned, and unplanned ( 46 ). Planned and unplanned: Knowledge transfer as a process where knowledge is transmitted from one person to another in the form of planned or natural ( 47 ).

Formal and informal: Knowledge transfer as an informal way through networks and social interactions in the workplace or formal way in an organization ( 47 , 48 )

Level: Knowledge transfer is a macro process, at the organizational level ( 42 ).

Steps: Knowledge transfer consists of the following steps: SECI: Socialization, Externalization, Combination, And Internalization ( 49 ).

Areas: Knowledge transfer areas include: Transfer of research findings ( 50 ). Technology transfer ( 51 ). Transfer of learning, Organizational transfer. ( 45 ).

Key point: The concept of knowledge transfer is at the macro level, where knowledge is spreading across sectors, units, or subsets of an organization ( 42 ).

Knowledge sharing attributes

Context: Knowledge sharing is an activity that involves transferring or disseminating knowledge from a person, group, or organization to another.

Purpose: The purpose of knowledge sharing is discovering tools for accessing knowledge inside and outside of organizations with a view to creating more effective management and organizational system ( 52 ).

Level: Knowledge sharing can be At the Individual level and micro ( 53 ). Among researchers, policymakers, service providers, stakeholders ( 54 ).

Activities: Sharing of knowledge is entirely conscious, with a person's desire, without any obligation ( 53 ).

Place for sharing Sharing of knowledge occurs at Conferences, social media, Media relation, Scholarly collaboration networks, Journal publication ( 55 )

Direction: It is a Mono directional process: A person's knowledge transforms into a form that can be understood, absorbed, and used by others. Bidirectional: Share information, ideas, suggestions and related organizational expertise with each other ( 56 ).

Key point: Common purpose and shared experiences between individuals, and Communication with others are taking place ( 56 ).

Kn owledge exchange attributes

Context: In the exchange of knowledge, collaborative problem solving between researchers and decision-makers takes place ( 54 ).

Purpose: The exchange of knowledge is to increase the effectiveness of networks and teams in complex environments ( 54 ). The exchange of knowledge to create new knowledge ( 57 ).

Form: Knowledge exchange is an active process: Researchers make knowledge available to users, and users also transfer knowledge to researchers. It Includes knowledge sharing (what employees give to others) and knowledge search (employees are seeking knowledge from others). ( 57 ).

Direction: There are bidirectional relations between researchers or knowledge producers and users.)58).

Key point: The interactions between researchers and decision-makers take place ( 57 ).

Knowledge dissemination attributes

Means for dissemination: Knowledge can be disseminated through articles, journals, conference lectures and other outputs ( 59 ).

The type of dissemination: Dissemination of knowledge is in the form of Knowledge, interventions and existing or recent methods ( 59 )

Direction: It is mono-directional, from the top to the bottom and from the expert ( 59 ).

Form: Knowledge dissemination is a planned process ( 59 ).

Knowledge publication attribute

Con text: One of the major academic duties to share their findings, and to interact with their peers and the general populace, via literal publication ( 60 ).

Purpose: The purpose of the publication is the Making-public of new knowledge ( 60 ).

Steps: Knowledge publication includes the following steps: Find the right journal, prepare the paper, and submit the paper ( 55 ).

Form: The publication of knowledge is in the form of Letter, rapid or short communications, Review papers, Full articles, Research elements (data, software, methods, Citable articles, in brief) ( 55 )

Key point: The publication is related to academic journals ( 55 ).

Knowledge Brokering

Knowledge brokering attributes.

Context: Knowledge brokering is one of the human forces behind knowledge transfer. It is a dynamic activity that goes well beyond the standard notion of transfer as a collection of activities that helps move information from a source to a recipient ( 61 ).

Purpose: Brokering focuses on identifying and bringing together people interested in an issue, people who can help each other develop evidence-based solutions. It helps build relationships and networks for sharing existing research and ideas and stimulating new work.” ( 62 ). Knowledge brokering encompasses a wide range of processes and practices that aim at establishing relationships and facilitating effective knowledge sharing and exchange ( 61 ).

Form: Knowledge brokering takes place as either formal or informal activities ( 61 ).

Type: Types of knowledge brokers are: Information Intermediary (Help Access to knowledge), Knowledge Intermediary (Help Understand and apply the knowledge), Knowledge Brokering (Help use of knowledge in decision making), Innovation Brokering (Changing Context). ( 61 )

Activities: Knowledge brokering activities are: uncovering the needs, ideas, activities, and processes of different knowledge environments in order to identify the best research, practices and tools that research partners need to capture, transfer, exchange and collaborate around knowledge ( 61 ).

Key point: It engages with obstacles that block the transfer of research into practice ( 61 ).

Knowledge storing: Knowledge assimilation, knowledge package, knowledge indexing, knowledge documentation.

Knowledge storage attributes

Context: Knowledge can be viewed as an item to be stored for future usage ( 34 ).

Purpose: Knowledge storage is In order to facilitate the assimilation of knowledge ( 63 ).

Type: Knowledge is stored in the form of individual and organizational knowledge, soft or hardstyle recording and retention ( 49 , 64 )

Form: Knowledge store as the form of documents, rules, cases, and diagrams ( 65 )

Method: Technical infrastructure such as modern informational hardware and software, human processes are necessary for storing knowledge ( 49 ).

Steps: Knowledge storage steps are: identify the knowledge in an organization, convert the identified knowledge to code, and index the identified knowledge for later retrieval ( 49 , 64 ).

Knowledge assimilation attributes

Context: A critical aspect of knowledge management is that of assimilation ( 66 , 67 ).

Purpose: 1. To take in and incorporate as one’s own; absorb 2. To bring into conformity with the customs, attitudes, etc. of a group 3. To convert to substances suitable for incorporation.

Steps: Knowledge assimilation steps are: Storage, massaged, organized, integrate, filtered, navigate ( 66 , 67 ).

Key point: Knowledge can be captured or created, but until it is assimilated it is not likely to receive extensive use ( 64 ).

Knowledge package attributes

Purposes : The purpose of the knowledge package is culling, cleaning and polishing, structuring, formatting, and/or indexing documents against a classification scheme ( 68 ).

Activities: Knowledge package activities include Authoring knowledge content, codifying knowledge into “knowledge objects” by adding context, developing local knowledge into “boundary objects” by deleting context, filtering and pruning content, and developing classification schemes ( 68 ).

Knowledge indexing attributes

Context: Knowledge index is to provide a summary about subject content; Indexing activity should be done as a pre analyzing process ( 69 ).

Purpose: The purpose of indexing is: organizing the Information in order to effectively use of information ( 70 ).

Steps: Knowledge index steps are: Review of documentation and establishment of subject matter, identify the core concept in documents, Referencing selected concepts by the terms of the indexing language ( 71 ).

Main actors: Librarian and intermediaries are the main actors for indexing of knowledge ( 71 ).

Knowle dge documentation attributes

Context: Preservation and documentation are ways to ensure the future existence of indigenous knowledge, which today is under threat of extinction ( 72 ). Facilitating re¬trieval knowledge is to take place from an organized data set (WIPO, 2016).

Purpose: The aim of documentation is to ensure the maintenance, use, and development of knowledge by present and future generations of peoples and communities ( 73 ).

Steps: Knowledge documentation steps are Knowledge identification, Knowledge fixation, and Knowledge classification ( 73 ).

Methods: The methods for documentation are Paper files, digital databases, archives, or libraries ( 73 ).

Main actors: Librarian and information professionals are the main actors for knowledge documentation ( 74 ).

Knowledge transfer : Knowledge sharing, knowledge exchange, knowledge dissemination, knowledge publication.

Knowledge capitalization : Knowledge commercialization, knowledge valorization.

Knowledge capitalization attributes

Context: Knowledge capitalization is the most important part of KM ( 75 ).

Purpose: It aims at building organizational memories that represent several views on expertise or activity (75.)

Activities: Capitalization is the process by which members of the community can identify, locate, model, store, access, use/reuse, share, update, and know-how to communicate the knowledge of the community ( 75 ).

Steps: Knowledge capitalization steps are: Knowledge extraction and formalization, Knowledge sharing, Knowledge reuse and appropriation, Memory evolution ( 75 ).

Form: Knowledge capitalization happens in the form of: Knowledge locate (identifying, localizing, characterizing, mapping, estimating, prioritizing), knowledge preserve (acquiring, modeling, formalizing, conserving), knowledge enhanced (accessing, disseminating, sharing, using more effectively, combining, and creating), knowledge actualized (appraising, updating, standardizing, enriching, knowledge managed (elaborate a vision: promote, inform, train, facilitate, organize, coordinate, encourage, motivate, measure, and follow up) ( 76 ).

Knowledge commercialization attributes

Context: Commercialization of knowledge is the Third mission of the university, Transfer of knowledge to industry ( 77 ).

Purpose: The purpose of commercialization is: Decrease independency to the public sector, Make commercial profit ( 78 ).

Direction: At the commercialization level Corporation between education and industry, dynamic improvement of production, and the economy system is taking place ( 78 ).

Steps: Knowledge commercialization steps include flowing: Idea generation, Idea evaluation, Idea development, Commercial analysis of the product, Market assessing, Commercialization ( 79 ).

Key point: Commercialization is not a linear process; it is a complex process ( 79 ).

Knowledge valorization attributes

Context: Valorization is a word of French origin translated as a “surplus value”. Valorization was framed in the context of the discourse of academic capitalism ( 80 ).

Purpose: The purpose of valorization is to transfer knowledge from one part to another for economic benefit” ( 81 ).

Path: The process of knowledge valorization is a long route that starts at universities ( 81 ). Valorization not only contributes to the availability of the results of academic research beyond academia but also involves the co-production of knowledge by academics and representatives of business ( 80 ).

Types: Types of valorization are societal (social) and economic ( 81 ).

Main actors: “Valorization is a cooperation between higher education institutions, government, and business players to agree on targeted investments in a number of key areas of regional innovation” ( 82 ).

Steps: Knowledge valorization steps are: Knowledge acquisition; amassing the relevant internal and external information required for the transfer of knowledge is collected and quickly deploying this information to its potential users, Knowledge processing; assess the market value of the relevant research and package the knowledge with market potential for business requirements, Knowledge dissemination; delivering of the knowledge package to business and assisting in the technology deployment ( 83 ).

Areas: Knowledge valorization areas include: education, Cooperation, contract research, R&D cooperation, and knowledge, and technology transfer, “entrepreneurship, “the production of successful high-tech start-ups” ( 84 ).

Key point: Knowledge-Economy Index which takes into account whether the environment is conducive for knowledge to be used effectively for economic development and Knowledge Index which measures a country’s ability to generate, adapt and diffuse knowledge ( 52 ). Valorization is broader than commercialization that is focused primarily on making a commercial profit ( 80 ).

Knowledge utilization: Knowledge adoption, knowledge adaptation, knowledge reuse.

Knowledge adoption attributes

Context: The adoption of knowledge is carried out in the field of innovation ( 46 ).

Purpose: Adoption is taking place in order to decision making about accept or refuse of innovation ( 46 ).

Steps: Knowledge adoption steps include: awareness about new knowledge, attitude formation, and decision about accept or refuse of innovation or new knowledge, implement a new idea or confirm accepted decision ( 46 ).

Key point: User motivation for use or rejection, resistance rate about new knowledge, consistency to the policy is determining factors in the knowledge adoption process ( 85 ).

Knowledge adaptation attributes

Context: The adaptation of knowledge is related to the results of the research, and this step is critical to the success of the knowledge transfer process ( 86 , 87 ).

Purpose: The goal is to make the results accessible and understandable by the users ( 86 , 87 ).

Key point: This step affects the user's decision to accept the knowledge generated by the researchers. Also, the availability of research results does not necessarily guarantee acceptance and use by users. Many authors have argued that the form of presentation of research results can be a motivation or obstacle to accepting knowledge in the educational community ( 87 ).

Knowledge reuse attributes

Context: It is a central issue for companies in order to avoid reinventing the wheel over and over again ( 89 ). The effective reuse of knowledge is arguably a more frequent organizational concern and one that is clearly related to organizational effectiveness ( 89 ).

Purpose: Knowledge reuse is taking place for sharing best practices or helping others solve common technical problems ( 88 ).

Steps: Knowledge reuse steps include: Capturing or documenting knowledge, packaging knowledge for reuse, Distributing or disseminating knowledge (providing people with access to it), and Reusing knowledge ( 35 ).

Activities: Knowledge reuse activities are followings: recall (that information has been stored, in what location, under what index or classification scheme) and recognition (that the information meets the users’ needs), as well as actually applying the knowledge ( 90 ).

Agent: There are three major roles in the knowledge reuse process: knowledge producer—the originator and documenter of knowledge, who records explicit knowledge or makes tacit knowledge explicit, knowledge intermediary—who prepares knowledge for reuse by eliciting it, indexing it, summarizing it, sanitizing it, packaging it, and who performs various roles in dissemination and facilitation, and knowledge consumer—the knowledge reuser, who retrieves the knowledge content and applies it in some way ( 91 ).

Key point: Successful knowledge transfer or reuse requires a complete solution. It is not just a matter of providing access to information technology and repositories. It also means careful attention to the design of incentives for contributing to and using repositories and to the roles of intermediaries to develop and maintain repositories and to facilitate the process of reuse ( 89 ).

Knowledge translation attributes

Context: The translation is the process of putting research findings and the products of research into the hands of key audiences. It is the art of weaving together processes of research and practice ( 92 ).

Purpose: Knowledge Translation is impact-oriented- the overarching goal of KT is to improve systems, practices, and ultimately lead to better outcomes ( 93 ).

Activity: Knowledge Translation includes multiple activities- Researchers need to go beyond mere dissemination and publication of results to multiple engagements to effect knowledge uptake ( 93 ).

Direction: Knowledge translation is a nonlinear process- it is also a complex process with multiple players, it also needs multidirectional communications ( 93 ).

Agent: Knowledge translation is an interactive process- the interactions between knowledge producers and knowledge users should be continuous. KT requires ongoing collaborations among relevant parties- collaboration, relationships, and trust among parties ( 92 ).

Steps: Knowledge translation includes all steps between the creation of new knowledge and its application ( 93 ).

Key point: It emphasizes the use of research-generated knowledge ( 93 ).

Knowledge management attributes

Context: Knowledge management is the process of transferring information and intellectual assets to a stable value. And it is related to making knowledge suitable for the correct processor, such as a human being or a computer, at the right time and at the right cost ( 94 ).

Purpose: The purpose of knowledge management is to create the knowledge that can be used by more than one person, for example, for the organization as a whole, or sharing knowledge between its members ( 94 ). Help to promote the use and sharing of data and information in decision making ( 95 ).

Activity: Knowledge management involves planning, organizing, and controlling individuals, processes, and systems to ensure that knowledge capital is promoted and applied effectively ( 33 ).

Type: Knowledge management has multidisciplinary nature, which includes: organizational science, cognitive science, information technology, linguistics, technical writing, ethnology and sociology, teaching, Communication studies, collaborative technologies such as computer-based collaborative activities, intranets, extranets, portals, and other network technologies ( 96 ).

Path: Under the knowledge management, the information becomes applicable to the knowledge and is applicable to the people who can use it ( 97 ).

Steps: Knowledge management steps involve: obtaining, organizing, managing, and disseminating knowledge in an organization in order to perform tasks faster, reuse best practices, and reduce costs twice ( 49 ). The process of finding, selecting, organizing, importing, and providing information in order to help raise the understanding of employees in a particular area ( 98 ).

Form: Knowledge management has two main aspects: knowledge as an obvious concern that reflects on organizational strategies, policies, and practices. On the other hand, it takes into account the relationships between intellectual capital (both apparently recorded and implicit in the form of personal knowledge) and Positive business results ( 99 ).

Studies have examined one or a few concepts in the field of knowledge management. Through this study, we were able to investigate all of the concepts related to knowledge management as far as possible. By criticizing and comparing the evidence and definitions relating to them, based on semantic proximity, we divided them into related categories and, clarify the boundary among them. We realized that many concepts had not found their appropriate place in the KM process, and there are no proper definitions of them. Therefore, it is necessary to redefine some of the concepts and the correct placement in the structure and operation of knowledge management. We can use the results of this study as the basis and the first step in developing a comprehensive model that includes all the concepts related to knowledge management and for determining the relationship among them and with other educational development concepts.

This study aimed to clarify the concepts in the knowledge management area. Through critically reviewing the literature in this field, we were able to identify the concepts and realize their attributes. Therefore, we came to a new interpretation of the concepts. At the end of the study, we concluded that some of the concepts have not been properly defined and are not properly located in the knowledge management field, and their application is uncertain. Regarding the identified gaps, there is a need to comprehensively study that consider all of these in the direction of knowledge management, show their application in a comprehensive model and, if necessary, redefined them, such as study can complement our work.

Acknowledgment

This article is a part of the dissertation entitled Educational Development with Approach on Knowledge Management. The authors would like to appreciate everyone who assisted them in this research.

Conflict of Interests

The authors declare that they have no competing interests.

Cite this article as: Yazdani Sh, Bayazidi S, Mafi AA. The current understanding of knowledge management concepts: A critical review. Med J Islam Repub Iran. 2020 (28 p);34:127. https://doi.org/10.34171/mjiri.34.127

Conflicts of Interest: None declared

Funding: None

• Writing on global warming
• Outline for human trafficking research paper
• Gay marriage paper tips
• Research paper about pros and cons of abortion
• Work ethics research paper
• Writing a bullying prevention research paper
• Immigration in US: writing ideas
• Writing companies
• Ordering tips to notice
• How to buy a research paper
• Research paper on domestic vilence
• Research paper on drug addiction
• Childhood obesity research paper
• Ideas for knowledge management research paper
• Paper on marriage couple and family counseling
• Research paper on gun control law

Helpful Ideas

Ideas for a research paper on knowledge management.

Knowledge management is a very interesting field that can be useful for many different people to learn about. If you’re required to compose a research paper on knowledge management, the first step of your task will be to select a decent narrow topic to study. If you have difficulties with topic selection, you may get inspiration by examining a list of sample ideas.

A Collection of Knowledge Management Research Paper Topics

• Effective methods to make employees overcome their reluctance to ask questions if they don’t understand their tasks clearly.
• The importance of the lessons learned databases.
• Effective methods to motivate employees to share their knowledge.
• The importance of the maturity models for knowledge management.
• Effective methods to make employees use their tools for what they do best rather than for everything.
• Effective sets of metrics for monitoring and improving knowledge management programs.
• Effective methods of managing the glut of information in order to pay attention only to important matters.
• The usage of cognitive computing for the optimization of searching and sharing information.
• The best ways to share knowledge within a particular organization.
• The sharing of internal knowledge in consulting companies.

This is only a small list of possible topics related to knowledge management. If you want to see more ideas, go to this great website .

Sources to Help You with Selecting a Topic

• Your instructor. If you cannot find a good idea to write about, consult your project advisor. It’s likely that they’ll offer you a few interesting directions.
• Other students. If you have college friends who have already written papers on knowledge management, you may ask them for topic ideas too.
• Professional academic paper writers. You may hire a writer who specializes in composing management-related papers to provide you with a list of original topics.

As you can see, there are many questions related to knowledge management that you may discuss in your term paper. Approach the task of topic selection seriously and find something that will be really interesting and useful for you to research and write about. This way, the quality of your paper should be rather high.

Professional essay writing service - https://essaymill.com - get your essays written by expert essay writer.

Expert admission essay writing service - get your essays written by professional application essay writer.

Helpful Links

Did you know.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

Knowledge Management

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Save to Library
• Last »
• Sociology of Knowledge Follow Following
• Innovation statistics Follow Following
• E-learning Follow Following
• Innovation and Knowledge Management Follow Following
• Web 2.0 Follow Following
• Digital Humanities Follow Following
• Knowledge organization Follow Following
• Science, Technology and Society Follow Following
• Social Media Follow Following
• Information Science Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Publishing
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Management Research Paper Topics

The field of management is an extremely broad discipline that draws upon concepts and ideas from the physical and social sciences, particularly mathematics, philosophy, sociology, and psychology. Within business, the field of management includes research paper topics and ideas also common to marketing, economics, finance, insurance, transportation, accounting, computer technologies, information systems, engineering, and business law.

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% off with 24start discount code, 300 management research paper topics, corporate planning and strategic management.

Aggregate Planning B2B B2E Management Barriers to Entry Best Practices Brainstorming Business Plan Capacity Planning Content Management System Decision Rules and Decision Analysis Decision Support Systems Diversification Strategy Divestment Downsizing and Rightsizing Economies of Scale and Economies of Scope Environmentalism and Sustainability Exit Strategy Exporting and Importing Franchising Free Trade Agreements and Trading Blocs Futuring Gap Analysis Generic Competitive Strategies Globalization Goals and Goal Setting Group Decision Making Knowledge-Based View of the Firm Location Strategy Long Tail Macroenvironmental Forces Make-or-Buy Decisions Manufacturing Resources Planning Market Share Mergers and Acquisitions Miles and Snow Typology Multiple-Criteria Decision Making New Product Development Open and Closed Systems Operations Strategy Opportunity Cost Order-Winning and Order-Qualifying Criteria Porter’s Five Forces Model Product Life Cycle and Industry Life Cycle Production Planning and Scheduling Results-Only Work Environment Strategic Integration Strategic Planning Failure Strategic Planning Tools Strategy Formulation Strategy Implementation Strategy in the Global Environment Strategy Levels SWOT Analysis Synergy Upselling Zero-Based Budgeting

EMERGING TOPICS IN MANAGEMENT

Activity-Based Costing Affirmative Action Angel Investors and Venture Capitalists Artificial Intelligence Assessment Centers B2B B2E Management Balanced Scorecard Bar Coding and Radio Frequency Identification Business Process Reengineering Cafeteria Plan—Flexible Benefits Cellular Manufacturing Chaos Theory Coalition Building Communities of Interest/Communities of Practice Complexity Theory Concurrent Engineering and Design Consulting Contingency Approach to Management Continuing Education and Lifelong Learning Trends Corporate Governance Corporate Social Responsibility Customer Relationship Management Decision Support Systems Diversity Electronic Commerce Electronic Data Interchange and Electronic Funds Transfer Empowerment Enterprise Resource Planning Entrepreneurship Environmentalism and Sustainability Ethics Expatriates Expert Systems Five S Framework Flexible Spending Accounts Futuring Handheld Computers Health Savings Accounts Human Resource Information Systems Innovation Instant Messaging Intellectual Property Rights Intrapreneurship Knowledge-Based View of the Firm Leadership in Energy and Environmental Design Metadata or Meta-Analysis Mobile Commerce Multiple-Criteria Decision Making Non-Compete Agreements Outsourcing and Offshoring Paradigm Shift Popular Press Management Books Quality of Work Life Results-Only Work Environment Robotics Social Networking Spirituality in Leadership Succession Planning Telecommunications Vendor Rating Virtual Corporations Women and Minorities in Management

ENTREPRENEURSHIP

Angel Investors and Venture Capitalists Balance Sheets Brainstorming Break-Even Point Budgeting Business Plan Business Structure Cafeteria Plan—Flexible Benefits Case Method of Analysis Cash Flow Analysis and Statements Competitive Advantage Consumer Behavior Cost Accounting Customer Relationship Management Diversification Strategy Domestic Management Societies and Associations Due Diligence Economics Economies of Scale and Economies of Scope Effectiveness and Efficiency Financial Issues for Managers Financial Ratios First-Mover Advantage Futuring Gap Analysis Generic Competitive Strategies Income Statements Initial Public Offering Innovation Intellectual Property Rights International Business International Management Societies and Associations Intrapreneurship Inventory Management Joint Ventures and Strategic Alliances Knowledge Management Knowledge Workers Leveraged Buyouts Licensing and Licensing Agreements Location Strategy Macroenvironmental Forces Make-or-Buy Decisions Market Share Marketing Concept and Philosophy Marketing Research Miles and Snow Typology Mission and Vision Statements New Product Development Non-Compete Agreements Organizational Development Outsourcing and Offshoring Patents and Trademarks Planning Poison Pill Strategies Popular Press Management Books Porter’s Five Forces Model Pricing Policy and Strategy Problem Solving Process Management Product Design Product Life Cycle and Industry Life Cycle Profit Sharing Research Methods and Processes Scenario Planning Securities and Exchange Commission Shareholders Stakeholders Strategic Planning Tools Strategy Levels Succession Planning SWOT Analysis Synergy Technology Transfer Value Creation Venture Capital Virtual Organizations

FINANCIAL MANAGEMENT AND ACCOUNTING

Activity-Based Costing Angel Investors and Venture Capitalists Balance Sheets Balanced Scorecard Break-Even Point Budgeting Capacity Planning Cash Flow Analysis and Statements Corporate Social Responsibility Cost Accounting Cost-Volume-Profit Analysis Debt vs. Equity Financing Domestic Management Societies and Associations Due Diligence Economics Electronic Data Interchange and Electronic Funds Transfer Employee Benefits Employee Compensation Executive Compensation Exit Strategy Financial Issues for Managers Financial Ratios Flexible Spending Accounts Health Savings Accounts Income Statements Initial Public Offering Insider Trading Internal Auditing International Management Societies and Associations International Monetary Fund Inventory Types Leveraged Buyouts Licensing and Licensing Agreements Long Tail Make-or-Buy Decisions Management Control Nonprofit Organizations Opportunity Cost Patents and Trademarks Profit Sharing Purchasing and Procurement Risk Management Securities and Exchange Commission Stakeholders Succession Planning Venture Capital Zero-Based Budgeting

GENERAL MANAGEMENT TOPICS

Aggregate Planning The Art and Science of Management Autonomy B2B B2E Management Balanced Scorecard Barriers to Entry Best Practices Black Friday Brainstorming Budgeting Business Plan Business Structure Communication Competitive Advantage Competitive Intelligence Contingency Approach to Management Continuous Improvement Corporate Governance Corporate Social Responsibility Delegation Disaster Recovery Diversity Divestment Downsizing and Rightsizing Economics Effectiveness and Efficiency Electronic Commerce Empowerment Financial Issues for Managers Financial Ratios Forecasting Generic Competitive Strategies Globalization Goals and Goal Setting Human Resource Management Innovation International Management Knowledge-Based View of the Firm Knowledge Management Leadership Styles and Bases of Power Leadership Theories and Studies Line-and-Staff Organizations Logistics and Transportation Management Control Management Functions Management Information Systems Management Science Management Styles Management Thought Managing Change Mission and Vision Statements Motivation and Motivation Theory Operations Management Organization Theory Organizational Analysis and Planning Organizational Behavior Organizational Chart Organizational Culture Organizational Learning Organizational Structure Organizational Development Organizing Paradigm Shift Participative Management Patents and Trademarks Paternalism Pioneers of Management Planning Process Management Quality and Total Quality Management Request for Proposal/Quotation Social Networking Strategic Integration Strategy Formulation Strategy Implementation Strategy in the Global Environment Strategy Levels Subject Matter Expert Succession Planning Training Delivery Methods Trends in Organizational Change

HUMAN RESOURCE MANAGEMENT

Affirmative Action Artificial Intelligence Assessment Centers Autonomy Nonverbal Communication Brainstorming Cafeteria Plan—Flexible Benefits Coalition Building Communication Continuing Education and Lifelong Learning Trends Discrimination Diversity Downsizing and Rightsizing Electronic Data Interchange and Electronic Funds Transfer Employee Assistance Programs Employee Benefits Employee Compensation Employee Evaluation and Performance Appraisals Employee Handbook and Orientation Employee Recruitment Employee Screening and Selection Employment Law and Compliance Empowerment Executive Compensation Flexible Spending Accounts Group Dynamics Health Savings Accounts Human Resource Information Systems Human Resource Management Japanese Management Job Analysis Knowledge-Based View of the Firm Knowledge Workers Mentoring Morale Motivation and Motivation Theory Nepotism Non-Compete Agreements Organizational Behavior Organizational Chart Organizational Culture Performance Measurement Personality and Personality Tests Privacy, Privacy Laws, and Workplace Privacy Quality of Work Life Reinforcement Theory Results-Only Work Environment Safety in the Workplace Scalable or JIT Workforce Sensitivity Training Social Networking Stress Succession Planning Sweatshops Task Analysis Teams and Teamwork Theory X and Theory Y Theory Z Time Management Training Delivery Methods Virtual Organizations Women and Minorities in Management

INNOVATION AND TECHNOLOGY

Artificial Intelligence Bandwidth Bar Coding and Radio Frequency Identification Communication Competitive Intelligence Complexity Theory Computer Networks Computer Security Computer-Aided Design and Manufacturing Computer-Integrated Manufacturing Content Management System Data Processing and Data Management Decision Rules and Decision Analysis Decision Support Systems Delegation Electronic Commerce Electronic Data Interchange and Electronic Funds Transfer Environmentalism and Sustainability Experience and Learning Curves Expert Systems Forecasting Fuzzy Logic Handheld Computers Information Assurance Innovation The Internet Knowledge Centers Knowledge Management Knowledge Workers Leadership in Energy and Environmental Design Management Information Systems Manufacturing Control via the Internet Metadata or Meta-Analysis Mobile Commerce Nanotechnology Product Design Project Management Robotics Service-Oriented Architecture Technology Management Technology Transfer Telecommunications Virtual Corporations Virtual Organizations Web 2.0 WiMax

INTERNATIONAL AND GLOBAL MANAGEMENT

B2B Competitive Advantage Diversity European Union Expatriates Exporting and Importing First-Mover Advantage Franchising Free TradeAgreements and Trading Blocs Futuring Globalization International Business International Management International Management Societies and Associations International Monetary Fund International Organization for Standards Japanese Management Licensing and Licensing Agreements Location Strategy Macroenvironmental Forces Outsourcing and Offshoring Patents and Trademarks Popular Press Management Books Profit Sharing Strategy in the Global Environment Sweatshops Transnational Organization Value-Added Tax Vendor Rating Virtual Organizations World-Class Manufacturer

LEADERSHIP RESEARCH TOPICS

The Art and Science of Management Assessment Centers Best Practices Communication Contingency Approach to Management Corporate Governance Corporate Social Responsibility Delegation Domestic Management Societies and Associations Entrepreneurship Executive Compensation Expert Systems Goals and Goal Setting Human Resource Management International Management Societies and Associations Japanese Management Job Analysis Joint Ventures and Strategic Alliances Knowledge Management Knowledge Workers Leadership Styles and Bases of Power Leadership Theories and Studies Line-and-Staff Organizations Management and Executive Development Management Functions Management Levels Management Styles Management Thought Managing Change Mechanistic Organizations Mentoring Mission and Vision Statements Morale Motivation and Motivation Theory Open and Closed Systems Operant Conditioning Organizational Culture Paradigm Shift Participative Management Personality and Personality Tests Pioneers of Management Problem Solving Reinforcement Theory Sensitivity Training Span of Control Spirituality in Leadership Strategy Formulation Succession Planning Teams and Teamwork Theory X and Theory Y Theory Z Women and Minorities in Management

LEGAL ISSUES

Affirmative Action Cafeteria Plan—Flexible Benefits Computer Networks Computer Security Corporate Governance Corporate Social Responsibility Discrimination Diversity Downsizing and Rightsizing Due Diligence Electronic Data Interchange and Electronic Funds Transfer Employee Assistance Programs Employee Benefits Employee Compensation Employee Evaluation and Performance Appraisals Employee Recruitment Employee Screening and Selection Employment Law and Compliance Ethics Executive Compensation Human Resource Management Insider Trading Intellectual Property Rights Job Analysis Leveraged Buyouts Management Audit Management Control Mergers and Acquisitions Nepotism Non-Compete Agreements Patents and Trademarks Personality and Personality Tests Privacy, Privacy Laws, and Workplace Privacy Quality of Work Life Risk Management Safety in the Workplace Stress Succession Planning Sunshine Laws Sweatshops Technology Transfer Whistle Blower Women and Minorities in Management

MANAGEMENT INFORMATION SYSTEMS

Balanced Scorecard Bandwidth Bar Coding and Radio Frequency Identification Barriers to Entry Complexity Theory Computer Networks Computer Security Computer-Aided Design and Manufacturing Computer-Integrated Manufacturing Content Management System Data Processing and Data Management Decision Rules and Decision Analysis Decision Support Systems Distribution and Distribution Requirements Planning Electronic Commerce Electronic Data Interchange and Electronic Funds Transfer Service-Oriented Architecture Statistical Process Control and Six Sigma Systems Design, Development, and Implementation Technology Management Technology Transfer

MANAGEMENT SCIENCE AND OPERATIONS RESEARCH

Bar Coding and Radio Frequency Identification Business Process Reengineering Computer-Aided Design and Manufacturing Concurrent Engineering and Design Decision Rules and Decision Analysis Decision Support Systems Distribution and Distribution Requirements Planning Expert Systems Location Strategy Logistics and Transportation Maintenance Make-or-Buy Decisions Manufacturing Resources Planning Models and Modeling Multiple-Criteria Decision Making New Product Development Operating System Operations Management Operations Scheduling Operations Strategy Product Design Production Planning and Scheduling Productivity Concepts and Measures Product-Process Matrix Project Management Purchasing and Procurement Quality and Total Quality Management Research Methods and Processes Reverse Supply Chain Logistics Scenario Planning Service Operations Service Process Matrix Simulation Statistical Process Control and Six Sigma Statistics Subject Matter Expert Systems Analysis Systems Design, Development, and Implementation Technology Transfer Warehousing and Warehouse Management World-Class Manufacturer

PERFORMANCE MEASURES AND ASSESSMENT

Activity-Based Costing Balance Sheets Balanced Scorecard Benchmarking Best Practices Break-Even Point Budgeting Cash Flow Analysis and Statements Continuous Improvement Cost Accounting Cost-Volume-Profit Analysis Cycle Time Debt vs. Equity Financing Due Diligence Effectiveness and Efficiency Executive Compensation Financial Issues for Managers Financial Ratios Forecasting Gap Analysis Goals and Goal Setting Management Audit Management Control Management Information Systems Market Share Multiple-Criteria Decision Making Nepotism Order-Winning and Order-Qualifying Criteria Performance Measurement Pricing Policy and Strategy Profit Sharing Simulation Stakeholders Value Analysis Value Chain Management Value Creation Vendor Rating Zero-Based Budgeting Zero Sum Game

PERSONAL GROWTH AND DEVELOPMENT FOR MANAGERS

The Art and Science of Management Brainstorming Coalition Building Communication Consulting Contingency Approach to Management Continuing Education and Lifelong Learning Trends Continuous Improvement Customer Relationship Management Delegation Diversity Employee Assistance Programs Empowerment Entrepreneurship Facilitator Feedback Goals and Goal Setting Group Dynamics Intrapreneurship Knowledge Workers Leadership Styles and Bases of Power Managing Change Mentoring Morale Motivation and Motivation Theory Multimedia Organizing Participative Management Personality and Personality Tests Planning Popular Press Management Books Problem Solving Profit Sharing Safety in the Workplace Sensitivity Training Spirituality in Leadership Strategic Planning Tools Stress Succession Planning SWOT Analysis Teams and Teamwork Time Management Trends in Organizational Change Value Creation

PRODUCTION AND OPERATIONS MANAGEMENT

Activity-Based Costing Aggregate Planning Bar Coding and Radio Frequency Identification Benchmarking Break-Even Point Business Process Reengineering Cellular Manufacturing Computer-Aided Design and Manufacturing Computer-Integrated Manufacturing Concurrent Engineering and Design Continuous Improvement Cost-Volume-Profit Analysis Decision Rules and Decision Analysis Decision Support Systems Distribution and Distribution Requirements Planning Domestic Management Societies and Associations Five S Framework Flexible Manufacturing Forecasting Industrial Relations International Management Societies and Associations Inventory Management Inventory Types Japanese Management Layout Lean Manufacturing and Just-in-Time Production Location Strategy Logistics and Transportation Long Tail Maintenance Make-or-Buy Decisions Management Awards Manufacturing Control via the Internet Manufacturing Resources Planning Market Share New Product Development Operations Management Operations Scheduling Operations Strategy Order-Winning and Order-Qualifying Criteria Outsourcing and Offshoring Participative Management Poka-Yoke Popular Press Management Books Porter’s Five Forces Model Production Planning and Scheduling Productivity Concepts and Measures Product-Process Matrix Project Management Purchasing and Procurement Quality Gurus Quality and Total Quality Management Reverse Supply Chain Logistics Robotics Safety in the Workplace Scalable or JIT Workforce Service Factory Service Industry Service Operations Service Process Matrix Simulation Statistical Process Control and Six Sigma Statistics Strategic Integration Supply Chain Management Synergy Teams and Teamwork Technology Management Technology Transfer Theory of Constraints Time-Based Competition Upselling Warehousing and Warehouse Management World-Class Manufacturer

QUALITY MANAGEMENT AND TOTAL QUALITY MANAGEMENT

Communication Customer Relationship Management Domestic Management Societies and Associations Five S Framework Gap Analysis Goals and Goal Setting Innovation International Management Societies and Associations Japanese Management Management Awards Manufacturing Resources Planning Marketing Research Operations Strategy Opportunity Cost Order-Winning and Order-Qualifying Criteria Outsourcing and Offshoring Participative Management Popular Press Management Books Productivity Concepts and Measures Quality Gurus Quality and Total Quality Management Quality of Work Life Statistical Process Control and Six Sigma Strategic Planning Tools Teams and Teamwork Value Analysis Value Creation Vendor Rating World-Class Manufacturer

SUPPLY CHAIN MANAGEMENT

Activity-Based Costing Business Process Reengineering Capacity Planning Cellular Manufacturing Coalition Building Communication Competitive Advantage Competitive Intelligence Computer Networks Computer-Integrated Manufacturing Conflict Management and Negotiation Customer Relationship Management Cycle Time Decision Support Systems Distribution and Distribution Requirements Planning Economies of Scale and Economies of Scope Effectiveness and Efficiency Electronic Commerce Electronic Data Interchange and Electronic Funds Transfer Enterprise Resource Planning Expert Systems Fulfillment Group Dynamics Industrial Relations Inventory Management Inventory Types Joint Ventures and Strategic Alliances Lean Manufacturing and Just-in-Time Production Location Strategy Logistics and Transportation Long Tail Make-or-Buy Decisions Manufacturing Resources Planning Market Share Multiple-Criteria Decision Making New Product Development Operations Management Operations Scheduling Operations Strategy Organic Organizations Organizing Poka-Yoke Problem Solving Process Management Product Design Product Life Cycle and Industry Life Cycle Production Planning and Scheduling Productivity Concepts and Measures Product-Process Matrix Purchasing and Procurement Quality and Total Quality Management Reverse Auction Reverse Supply Chain Logistics Risk Management Span of Control Stakeholders Teams and Teamwork Vendor Rating Warehousing and Warehouse Management

TRAINING AND DEVELOPMENT

Artificial Intelligence Assessment Centers Autonomy Concurrent Engineering and Design Conflict Management and Negotiation Consulting Contingency Approach to Management Continuing Education and Lifelong Learning Trends Continuous Improvement Corporate Social Responsibility Delegation Domestic Management Societies and Associations Downsizing and Rightsizing Employee Evaluation and Performance Appraisals Employee Handbook and Orientation Goals and Goal Setting Group Decision Making Human Resource Management Innovation Instant Messaging International Management Societies and Associations Job Analysis Knowledge Management Knowledge Workers Management and Executive Development Management Audit Marketing Communication Mission and Vision Statements Morale Motivation and Motivation Theory Multimedia Multiple-Criteria Decision Making Organizational Culture Organizational Learning Organizing Participative Management Personality and Personality Tests Popular Press Management Books Problem Solving Project Management Safety in the Workplace Sensitivity Training Simulation Stress Succession Planning SWOT Analysis Teams and Teamwork Training Delivery Methods Virtual Organizations Women and Minorities in Management

Management has applications in a wide variety of settings and is not limited to business domains. Management tools, as well as the art and science of management, find applications wherever any effort must be planned, organized, or controlled on a significant scale. This includes applications in government, the cultural arts, sports, the military, medicine, education, scientific research, religion, not-for-profit agencies, and in the wide variety of for-profit pursuits of service and manufacturing. Management takes appropriate advantage of technical developments in all the fields it serves.

The growth of the discipline of management has also led to specialization or compartmentalization of the field. These specialties of management make learning and study easier, but at the same time make broad understanding of management more difficult. It is particularly challenging to the entrepreneur and the small business owner to master the subject areas, yet this group is compelled to excel at all management functions to further their business’s success. Management specialties have grown to such an extent it is difficult for any single manager to fully know what management is all about. So rapid have been the strides in recent years in such subjects as decision making, technology, the behavioral sciences, management information systems, and the like, to say nothing of proliferating legislative and governmental regulations affecting business, that constant study and education is required of all managers just to keep current on the latest trends and techniques. Thus, managers and executives need a comprehensive management online reference source to keep up-to-date. Having the management essays and research papers in one comprehensive site saves valuable research time in locating the information.

In the growing age of specialists, there is a growing lack of generalists. Typically, a business manager spends a large percentage of their career developing a great familiarity and proficiency in a specialized field, such as sales, production, shipping, or accounting. The manager develops a very specialized knowledge in this area but may develop only a peripheral knowledge of advances in other areas of management. Yet as these individuals are promoted from a specialist-type position up the organizational chart to a more administrative or generalist supervisory or leadership position, the person with newly enlarged responsibilities suddenly finds that their horizon must extend beyond the given specialty. It must now include more than just a once-superficial understanding of all aspects of managing, including purchasing, manufacturing, advertising and selling, international management, quantitative techniques, human resources management, public relations, research and development, strategic planning, and management information systems. The need for broader management understanding and comprehension continues to increase as individuals are promoted.

This site has as its goal to bridge this gap in understanding and to offer every executive, executive-aspirant, management consultant, and educator and student of management, both comprehensive and authoritative information on all the theories, concepts, and techniques that directly impact the job of management. This reference source strives to make specialists aware of the other functional areas of the management discipline and to give the top manager or administrator who occupies the general manager position new insights into the work of the specialists whom he or she must manage or draw upon in the successful management of others. In addition, this site proposes to make all practitioners aware of the advances in management science and in the behavioral sciences. These disciplines touch upon all areas of specialization because they concern the pervasive problems of decision-making and interpersonal relations.

Every effort has been made to achieve comprehensiveness in choice and coverage of subject matter. The essays provided frequently go far beyond mere definitions and referrals to other sources. They are in-depth treatments, discussing background, subject areas, current applications, and schools of thought. In addition, information may be provided about the kinds of specialists who use the term in a given organization, the degree of current acceptance, and the possibilities for the future as the subject undergoes further development and refinement. Longer essays frequently provide charts, graphs, or examples to aid in understanding the topic.

Browse More Management Research Paper Topics:

• Business Management
• Financial Management
• Operations Management
• Marketing Management
• Performance Management
• Security Management
• Total Quality Management
• Human Resource Management

ORDER HIGH QUALITY CUSTOM PAPER

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Research: How Different Fields Are Using GenAI to Redefine Roles

• Maryam Alavi

Examples from customer support, management consulting, professional writing, legal analysis, and software and technology.

The interactive, conversational, analytical, and generative features of GenAI offer support for creativity, problem-solving, and processing and digestion of large bodies of information. Therefore, these features can act as cognitive resources for knowledge workers. Moreover, the capabilities of GenAI can mitigate various hindrances to effective performance that knowledge workers may encounter in their jobs, including time pressure, gaps in knowledge and skills, and negative feelings (such as boredom stemming from repetitive tasks or frustration arising from interactions with dissatisfied customers). Empirical research and field observations have already begun to reveal the value of GenAI capabilities and their potential for job crafting.

There is an expectation that implementing new and emerging Generative AI (GenAI) tools enhances the effectiveness and competitiveness of organizations. This belief is evidenced by current and planned investments in GenAI tools, especially by firms in knowledge-intensive industries such as finance, healthcare, and entertainment, among others. According to forecasts, enterprise spending on GenAI will increase by two-fold in 2024 and grow to $151.1 billion by 2027 .

• Maryam Alavi is the Elizabeth D. & Thomas M. Holder Chair & Professor of IT Management, Scheller College of Business, Georgia Institute of Technology .

Partner Center

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

• Machine learning
• Social justice
• Black holes
• Classes and programs

Departments

• Aeronautics and Astronautics
• Brain and Cognitive Sciences
• Architecture
• Political Science
• Mechanical Engineering

Centers, Labs, & Programs

• Abdul Latif Jameel Poverty Action Lab (J-PAL)
• Picower Institute for Learning and Memory
• Lincoln Laboratory
• School of Architecture + Planning
• School of Engineering
• School of Humanities, Arts, and Social Sciences
• Sloan School of Management
• School of Science
• MIT Schwarzman College of Computing

Large language models use a surprisingly simple mechanism to retrieve some stored knowledge

Press contact :.

Previous image Next image

Large language models, such as those that power popular artificial intelligence chatbots like ChatGPT, are incredibly complex. Even though these models are being used as tools in many areas, such as customer support, code generation, and language translation, scientists still don’t fully grasp how they work.

In an effort to better understand what is going on under the hood, researchers at MIT and elsewhere studied the mechanisms at work when these enormous machine-learning models retrieve stored knowledge.

They found a surprising result: Large language models (LLMs) often use a very simple linear function to recover and decode stored facts. Moreover, the model uses the same decoding function for similar types of facts. Linear functions, equations with only two variables and no exponents, capture the straightforward, straight-line relationship between two variables.

The researchers showed that, by identifying linear functions for different facts, they can probe the model to see what it knows about new subjects, and where within the model that knowledge is stored.

Using a technique they developed to estimate these simple functions, the researchers found that even when a model answers a prompt incorrectly, it has often stored the correct information. In the future, scientists could use such an approach to find and correct falsehoods inside the model, which could reduce a model’s tendency to sometimes give incorrect or nonsensical answers.

“Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them. This is one instance of that,” says Evan Hernandez, an electrical engineering and computer science (EECS) graduate student and co-lead author of a paper detailing these findings .

Hernandez wrote the paper with co-lead author Arnab Sharma, a computer science graduate student at Northeastern University; his advisor, Jacob Andreas, an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); senior author David Bau, an assistant professor of computer science at Northeastern; and others at MIT, Harvard University, and the Israeli Institute of Technology. The research will be presented at the International Conference on Learning Representations.

Finding facts

Most large language models, also called transformer models, are neural networks . Loosely based on the human brain, neural networks contain billions of interconnected nodes, or neurons, that are grouped into many layers, and which encode and process data.

Much of the knowledge stored in a transformer can be represented as relations that connect subjects and objects. For instance, “Miles Davis plays the trumpet” is a relation that connects the subject, Miles Davis, to the object, trumpet.

As a transformer gains more knowledge, it stores additional facts about a certain subject across multiple layers. If a user asks about that subject, the model must decode the most relevant fact to respond to the query.

If someone prompts a transformer by saying “Miles Davis plays the. . .” the model should respond with “trumpet” and not “Illinois” (the state where Miles Davis was born).

“Somewhere in the network’s computation, there has to be a mechanism that goes and looks for the fact that Miles Davis plays the trumpet, and then pulls that information out and helps generate the next word. We wanted to understand what that mechanism was,” Hernandez says.

The researchers set up a series of experiments to probe LLMs, and found that, even though they are extremely complex, the models decode relational information using a simple linear function. Each function is specific to the type of fact being retrieved.

For example, the transformer would use one decoding function any time it wants to output the instrument a person plays and a different function each time it wants to output the state where a person was born.

The researchers developed a method to estimate these simple functions, and then computed functions for 47 different relations, such as “capital city of a country” and “lead singer of a band.”

While there could be an infinite number of possible relations, the researchers chose to study this specific subset because they are representative of the kinds of facts that can be written in this way.

They tested each function by changing the subject to see if it could recover the correct object information. For instance, the function for “capital city of a country” should retrieve Oslo if the subject is Norway and London if the subject is England.

Functions retrieved the correct information more than 60 percent of the time, showing that some information in a transformer is encoded and retrieved in this way.

“But not everything is linearly encoded. For some facts, even though the model knows them and will predict text that is consistent with these facts, we can’t find linear functions for them. This suggests that the model is doing something more intricate to store that information,” he says.

Visualizing a model’s knowledge

They also used the functions to determine what a model believes is true about different subjects.

In one experiment, they started with the prompt “Bill Bradley was a” and used the decoding functions for “plays sports” and “attended university” to see if the model knows that Sen. Bradley was a basketball player who attended Princeton.

“We can show that, even though the model may choose to focus on different information when it produces text, it does encode all that information,” Hernandez says.

They used this probing technique to produce what they call an “attribute lens,” a grid that visualizes where specific information about a particular relation is stored within the transformer’s many layers.

Attribute lenses can be generated automatically, providing a streamlined method to help researchers understand more about a model. This visualization tool could enable scientists and engineers to correct stored knowledge and help prevent an AI chatbot from giving false information.

In the future, Hernandez and his collaborators want to better understand what happens in cases where facts are not stored linearly. They would also like to run experiments with larger models, as well as study the precision of linear decoding functions.

“This is an exciting work that reveals a missing piece in our understanding of how large language models recall factual knowledge during inference. Previous work showed that LLMs build information-rich representations of given subjects, from which specific attributes are being extracted during inference. This work shows that the complex nonlinear computation of LLMs for attribute extraction can be well-approximated with a simple linear function,” says Mor Geva Pipek, an assistant professor in the School of Computer Science at Tel Aviv University, who was not involved with this work.

This research was supported, in part, by Open Philanthropy, the Israeli Science Foundation, and an Azrieli Foundation Early Career Faculty Fellowship.

Share this news article on:

Press mentions.

Researchers at MIT have found that large language models mimic intelligence using linear functions, reports Kyle Wiggers for  TechCrunch . “Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them,” writes Wiggers. 

Previous item Next item

Related Links

• Evan Hernandez
• Jacob Andreas
• Language and Intelligence Group
• Computer Science and Artificial Intelligence Laboratory
• Department of Electrical Engineering and Computer Science

Related Topics

• Computer science and technology
• Artificial intelligence
• Human-computer interaction
• Computer Science and Artificial Intelligence Laboratory (CSAIL)
• Electrical Engineering & Computer Science (eecs)

Related Articles

Demystifying machine-learning systems

AI agents help explain other AI systems

3 Questions: Jacob Andreas on large language models

Solving a machine-learning mystery

More mit news.

Designing solutions to ensure equity in health care

Read full story →

Training manufacturing technologists to be future shop floor leaders

Characterizing social networks

MIT economics to launch new predoctoral fellowship program

Programming functional fabrics

Does technology help or hurt employment?

• More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

• Map (opens in new window)
• Events (opens in new window)
• People (opens in new window)
• Careers (opens in new window)
• Accessibility
• Social Media Hub
• MIT on Facebook
• MIT on YouTube
• MIT on Instagram