Social Learning Problems and Failures: A Socio-Cognitive Perspective Part 1: The Design Causes

Paper Title

Social Learning Problems and Failures: A Socio-Cognitive Perspective Part 1: The Design Causes

Abstract

Many of the problems and failures of Social Learning have been attributed to organisational and technical behavioural issues. The amount of money organisations spend on hiring and recruiting staff is of little benefit because the systems continue to fail. Steps can be taken to understand and solve these behavioural problems. This paper argues that, in most cases, these behavioural problems result from inadequate knowledge transfer design and analysis. These bad designs are attributed to the way organisations view their staff members and the function within them (i.e. frames of connection. These frames of connection cause faulty design choices and failures to perceive better design alternatives for knowledge transfer. The discussion of this paper seeks to demonstrate the need to refrain from connection, specifically to the knowledge transfer approach and change the information and knowledge management perspective. The socio-cognitive approach is introduced as a realistic view of humans and a way to change to help support organisations. The purpose of the paper is to demonstrate the need for the socio-cognitive approach. The second will present the basic concepts and principles of the socio-cognitive process and how it can be utilised in the design of knowledge transfer.

Author Keywords

Social Learning problems and failures, behavioural problems, design, socio-cognitive design 

General Terms

Design, Theory

1. Introduction

The major reason organisations have had so many failures and problems is the way managers and technical system designers view knowledge, and the function of knowledge within them (Bou-Llusar & Segarra-Ciprés, 2006; Chen & McQueen, 2010; Huang et al., 2010; Joia & Lemos, 2010; McLaughlin, 2010; Wilkesmann & Wilkesmann, 2011; Huang et al., 2012). These views are embedded in a development and implementation methodology which guides knowledge to be stored within systems. The purpose of this article is to provide other researchers with an overview of a design approach which is based on a more realistic view. This design approach is referred to as the Socio-Cognitive design. Socio-cognitive is a fairly recent research in information and knowledge management (Reihlen & Ringberg, 2013; Wrona et al., 2013), questing for knowledge transfer and social learning which are both more understanding. The approach is used for redesigning existing knowledge transfer. It is believed the utilisation of the socio-cognitive approach will contribute to solving many of the problems facing knowledge and substantially reduce the number of knowledge loss failures (Anonymous, 2008; Cattani et al., 2012; Daghfous et al., 2013; Joe et al., 2013; Tortoriello et al., 2013).

Knowledge transfer is made up of two jointly independent, but correlative interacting processes – the socio-cognitive process (Liao et al., 2010; Lin & Huang, 2010; Wei et al., 2010; Maden et al., 2013) and social learning (Hernández-Ramos & Bowker, 2007; Çelen & Hyndman, 2012; Lorenzo et al., 2012; Lahtinen, 2013). The socio-cognitive is contributed by the cognitive attributes of environment, observation and behaviour, as such, the interactions and relationships. The process suggests knowledge transfer is achieved by a cognitive design process aiming at the joint reflection and optimisation of thinking. The social learning process is concerned with engagement, exploration, value and influence. It is assumed the creativity and innovation outputs of the two processes are the result of joint interaction between these two. Thus, any design of knowledge transfer for social learning will need to include both.

The purpose of this paper is to demonstrate the need for the socio-cognitive knowledge transfer design presented in the first section. The second section presents the basic concepts and principles of socio-cognitive and how the process can be utilised in the knowledge transfer design. In this paper, knowledge transfer is viewed as an intervention strategy, which seeks to intervene how knowledge transfer for the purpose of improving organisation function and profit, for a change effort (O’Dell & Grayson Jr., 1999; Khriss et al., 2000; Osterloh & Frey, 2000; Song et al., 2003; Palacios-Marqués et al., 2013). The term knowledge transfer, implies someone who is changing, designing or redesigning, contributing towards problem-solving or decision making. Knowledge transfer is an example of the larger researcher scope for knowledge management strategy (Fern & Cardinal, 2003; McLaughlin, 2010). The paper is to stress to the reader the socio-cognitive aspect and to make the point, that the aspects of the socio-cognitive process cannot be isolated from social learning design.

The extant literature views knowledge transfer as an improvement in organisation innovation or productivity (Chang & Gurbaxani, 2012; Chan et al., 2014), specifically to the quality of products, services, and reduced costs (Tsai, 2001; Paton & McLaughlin, 2008; Siachou & Ioannidis, 2010; Cambra-Fierro et al., 2011; Maurer et al., 2011; Abbate et al., 2013; Sheng et al., 2013). Etc. Historically, the idea of knowledge transfer has included only the issues of power, manager, strategic and corporate level. These views are still present in the literature, however, the concept of knowledge transfer is expanding to include other concerns such as meaningful learning (Çelen & Hyndman, 2012; Lorenzo et al., 2012; Lahtinen, 2013; Lee & Bell, 2013; Stritch & Christensen, 2013), influence, social ties (Roberts & Sterling, 2012; Trapido, 2012; Zeng & Wei, 2012; Zhelyazkov, 2012; Zhuang et al., 2012; Shriver et al., 2013; Skvoretz, 2013), and opportunities for learning (Ekambaram & Johansen, 2011; Chen et al., 2012; Li, 2012; Salleh et al., 2012; Chan et al., 2014). 

2. Background

Alavi & Leidner (2001) summarised the results of research involving over 100 organisations, as follows: “However, there has been a growing interest in treating knowledge as a significant organisation resource.” In addition, support for the above grew in the related fields of management science, information technology and operation research. The implication of these findings suggested that if steps were not taken to understand and solve organisational problems, organisations would continue to spend much investment on the development of information systems because of the lack of understanding of the benefits and hence a higher rate of failure.

Many in the literature, argue such systems, such as knowledge management systems, and expert systems, still fail because of the result of inflexibility and systems being static repository systems, subsequentfailing to meeting the necessary requirements. The problem with existing technology designs was due to the influence of the technical design, in specific, focusing on skills, values and assumptions of workers linked to the organisation's perspective. Traditionally, such systems did not allow a feedback loop, and as such, most existing system designs failed because of a lack of recognition resulting in dysfunction in the social system. Another example is an organisation's reward system, such type of system may force or support human behaviour frame. The table below reflects the dimensions of the two main assumptions in the identity of human knowledge transfer:

No Assumption Driver

1 Organisation Humans act to meet target objectives

2 Technology Technological force

3. Conditions

3.1. Condition 1: Design and Implicit Theories

The information systems and technology literature clearly make assumptions about humans – for example, human view, humans are poor information processors as opposed to the organisation view, information flow must be controlled and managed driven by management and strategic level. Such design could be viewed from the technical perspective of how humans and organisations could change and adapt to the process. This type of condition is implicit because technicalists merely attempt to develop and carefully look at the detailed description prior to their assumptions and beliefs. The handbook by Malhotra (2004) and Huerta et al. (2012) clearly supports these conclusions.

 

Nevertheless, researchers have attempted to capture to main aspect of key concepts and assumptions based on two main well-established theories resource theory (Conner, 1991; Grant, 1991; Barney, 1996; Conner & Prahalad, 1996; Das & Teng, 2000), and knowledge based theory (Parsons, 1995; Foss, 1996; Grant, 1996; Spender, 1998; Sveiby, 2001; Spender, 2003; Nickerson & Zenger, 2004). Resource-based theory assumes knowledge is a resource for the organisation, paid in the form of wage, and organisations have a great deal of control over the workforce activities (Das & Teng, 2000; Van Witteloostuijn & Boone, 2006; Wan et al., 2010; Barney et al., 2011; Warnier et al., 2013)s. Knowledge-based theory assumes humans are responsible, self-achieving and take control of driving the work environment. Both these theories are viewed as management and managerial philosophies. Thus, these two theories can be perceived as a set of assumptions about the organisation's nature.

These basic assumptions about organisation knowledge shape workforce design and strategies. For example, resource-based theory would tend to create a tightly controlled and structured organisation with clear lines of command and control authority, in addition, to emphasising stability and control to obtain operational efficiency. On the other hand, knowledge-based theory assumptions would focus on creating a flexible organisation, thus allowing the workforce humans’ self-control and direction at all levels with innovation and creativity improvement for driving the organisation's effectiveness.

 

3.2. Condition 2: Concept of Responsibility

The concept of responsibility is a very important aspect of the knowledge-based theory, however, it has received very little discussion in the literature (Rodrigues & de Oliveira, 2010; Guadamillas-Gómez & Donate-Manzanares, 2011). The focus of this section will be on the concept of responsibility as it applies to anyone contributing in the knowledge transfer phase. The critical question is, who is responsible for the problem-solving and decision-making? Based on the available research, any contributors can facilitate or inhibit knowledge transfer through participation however, it is difficult to assume the responsibility because the outcome could either be positive or negative. Therefore, it could be argued that knowledge transfer efforts can be successful only if collaborators collaborate on responsibility for success. Such design should act in a way which retains and supports responsibility.

In the literature, organisation guides the efficiency, cost, and speed of input (Morey, 2001). This raises a number of issues. First, it highlights the issue of “who is the user?” Secondly, it highlights the question, of humans making decisions based on goal optimisation of efficiency, speed and strategy. It can be recognised, that each human has the responsibility, and however, rigorous detail is required to identify this. The literature highlights some attempts to initiate to implement greater responsibility: For example, chief knowledge officers are placed within organisations with primary responsibility for the design to establish a steering committee. In addition, facilitators are placed to enable power-sharing during the collaborative approach to overcome responsibility (Guns, 1997; Earl & Scott, 1999; Bontis, 2001; Cegarra-Hialarro et al., 2007; Kaplan, 2007).

 

3.3. Condition 3: Non-Cognitive View – Limited Frameworks

The third condition responsible for the inadequate design is the limited conceptualisation of the cognitive process (Reihlen & Ringberg, 2013; Nonaka et al., 2014). The primary targets for social learning, decision support, and socio-cognitive are the linked elements towards social learning (Lahtinen, 2013; Lee & Bell, 2013; Stritch & Christensen, 2013), decision making, knowledge collection, manipulation and transmission of knowledge. Upon completion of an analysis of cognitive behaviour requirements and flow, the socio-cognitive process is designed to reallocate knowledge processing and decision-making between humans and to create new and modify old ones to support the new reallocation. For example, situations exist and evolve with the need for collaborative design. In another example, problem-solving takes place in abstract scenarios and a common language, resulting in education and learning from all.

The major task from the management level is to translate the output of the strategic organisation view which is often, not clearly recognised from the operational level. This can be seen reflected in the early paper of Alavi & Leidner (2001) which set the foundations for the research field. The respondents from the research reflected middle managers and strategic and technical officers. The reciprocal part of this paper is that continuous researchers perceived management and strategic level to have such authority. However, as the research field evolved over time, the large reliance on strategy level is a result of the assumptions and goals between humans and knowledge transfer in order to develop meaningful understanding. This limited focus on the management perspective ignores the fact that knowledge transfer causes more changes within other variable levels. The strong association between management and organisational structure changes the workforce's attitudes and motivations. Many researchers from the management discipline have concluded that knowledge is a critical resource on which organisations operate. An aspect to contribute to improving this condition is to incorporate a cognitive processing view into a more complete comprehensive framework.

3.4. Condition 4: Limited Goal Orientation – Optimising the Organisation View

The systematic view by organisations and management strategists reflects their role in the organisation's view. Thus, the literature would be expected to find organisationalion view of the goal and intervention of knowledge transfer (i.e. improvement in product innovation). The current literature identifies the manager as the most efficient choicspecificallyfic for the better control mechanism. One point to note is the choice is made without the quality and concern of the workforce. The management literature clearly indicates the dominant goal of the organisation's view intervention. Many researchers suggested such drivers for greater efficiency and management of knowledge were the prime contributions of an organisation view. The knowledge management is a relatively mature a. However, field reseis exists on the effectiveness of socio-cognitive interventions in terms of cognitive optimisation. The effects highlight a lack of conc,ern resulting in continuous improvement and a need for quality of knowledge loss. This situation is usually utilised by the use of repository systems due to the assumption of achieving the same continuum (I.e. productivity, efficiency vs. workforce support). Some of the reviews and learnings from the conclusion of other researchers indicate viewing productivity, efficiency, and quality of knowledge is an inappropriate concept, and as such, the socio-cognitive view and organisation view are two different scales. The extant literature reflects an organisation system improvement as opposed to the workforce social system. Furthermore, European researchers (Palacios-Marqués et al., 2013; Sankowska, 2013) found there are a number of unexplored, alternatives for knowledge transfer. It is suggested that removing constraints on workforce behaviour provides an unfreezing force and subsequently, changes the way of behaving. This new type of freedom behaviour could facilitate change. The goal orientation must be broadened to include socio-cognitive development efforts to enhance knowledge transfer.

 

3.5. Condition 5: Limited Group Categories 

The literature highlights a classification system, resulting in commitment and requirement from managerial and knowledge management consultants to aid, advise, redesign and facilitate organisation design. The literature highlights the effects and results from middle management and higher level (Beliveau, 2012; Jiang & Aulakh, 2013; Patriotta et al., 2013), who received the main benefits of the knowledge transfer while, secondary staff members, operational workforce and clerks, were ignored (Joshi et al., 2007; Wu et al., 2007; Vijayakumar et al., 2008; Letmathe et al., 2012). This lack of concern for group categories, and classification systems resulted in a de-enrichment of the design. This suggests, that researchers were traditionally left with the conclusion, that existing research was designed and targeted a small set of users/participants, usually managers, onwards. The significance of the limited group categories and conditions is that today, organisations have changed and no longer operate in a tightly controlled environment. Other workforce levels on the periphery of knowledge transfer are ignored. This situation is very unfortunate for two reasons. Firstly, the workforce has a continual day-to-day understanding of the detailed base. Secondly, the workforce can determine how successful, for example, a knowledge management system will be. Little attention has been paid to the variety of group categories available and or those who contribute.

A related problem in the management literature often feeds on information on those who can take action. The magnitude of this still problem still remains. In research, they argue in order for productivity to increase, feedback is suggested to have a potentially great impact, in addition, when the workforce obtains meaningful feedback on their performance, this results in a driver for motivation (Riss et al., 2007). Therefore, the findings indicate, if no feedback is offered to the workforce, contradictory appears as little improvement in organisation efficiency will result. Overall, this suggests a need to take a total classification in order to improve the design by taking a degree level of all humans affected and involved in the knowledge transfer.

3.6. Condition 6: Limited Reflective Systems and Technologies

The approach to the development of the knowledge transfer process treats design as a systematic process in a static environment. Organisations were traditionally assumed and defined as a stabilised environment and followed specific well-defined goals and visions (Xu, 2005). This viewpoint overlooks many of the dynamic environmental properties in which problem-solving and decision-making are made (Hinterhuber, 2007; Parent et al., 2007). For example, the early literature can be labelled with rational and static types of behaviour, as attempts can be seen to highlight problematic issues, however, these gradually became distorted through the use of managerial and command and control issues. Such control issues are raised amongst the higher level and the imbalance of power. The literature also highlights there is no ideal knowledge management system, given the variety of types of available systems. However, each and every organisation will have its own preferences, as no one size fits all. Existing systems and technologies designed were limited to specific roles and positions of power. This means such systems were limited and constrained.

Also, an aspect shows the unreflective mode where the organisation continues to change and does not remain in a stable state. Since globalisation, more attention needs to be paid towards action and behavioural systems (Shin & Kook, 2014). For example, previous systems were linear and did not reflect new problems such as the iterative nature process rather than a static view (Shin & Kook, 2014). Many existing organisations use control techniques based on this view. This section seeks to address not negating the importance of design planning and control techniques, however, to stress the current emphasis on static view must be replaced by a more dynamic approach and realistic view. 

       

There is growing research on how such knowledge management systems could support and influence further user base (Greco et al., 2013; Lin et al., 2013; Sampson & Zervas, 2013; Sha et al., 2013; Sutanto & Jiang, 2013; Wang et al., 2013). Such systems can overlap the core business and information systems engineering, in specific, Computer Supported Cooperative Work (CSCW). Traditionally, CSWS studied the interdependency of humans using systems (Mantei, 1989; Aiken & Carlisle, 1992; Ackermann & Eden, 1994; Petrovic & Krickl, 1994; Skyrme, 1995; Moffett & Patterson, 1998; Sun & Lin, 2003). However, today, a new form of CSCW takes place through systems designed as open innovation, social crowdsourcing and the inclusion of external experts into organisation settings. This has resulted in an important implication for organisation structure, driving motivation and management (Gutwin, 2005; Hoeben & Stappers, 2005; Francescato et al., 2007).

 

Such social knowledge technology systems support interaction and learning by using systems and the Business and Information Systems Engineering literature, within the last ten years, refer to this as social computing and or social software. The seminal works of such terms can be traced to the idea of Memex by Vannevar Bush in 1945. Further research focuses on creating value from sourcing, and producing vision to action and collaborative systems for managing complexity (Hiltz et al., 2011).  (Pawlowski et al., 2014).

3.7. Condition 7: Social Learning Behaviour in Development Use

The literature has primarily focused on the organisation under development, and the role of social learning is treated as a constraint on the development of learning or wholly ignored. During the knowledge transfer, the poor relationship collaboration between managers led to dissatisfaction in conceptualising the effects of knowledge transfer interventions. The subject of social learning behaviour is a multi-disciplinary field in different disciplines, including psychology, sociology, and organisation behaviour and information systems. Social learning is concerned with explaining human behaviour towards learning (Çelen & Hyndman, 2012; Howorth et al., 2012; Lahtinen, 2013). This includes acquiring, transferring, exchanging, using, interpreting and sharing knowledge. This area describes the produced knowledge, absorptive, utilisation and shared innovation. Examples are absorptive capacity (Nicotra et al., 2013; Nowak, 2013; Oberschmidt, 2013; Ofstein et al., 2013; Posen & Chen, 2013; Soo et al., 2013; Steenrod & Lin, 2013; Tortoriello, 2014), hidden knowledge (Chen et al., 2009; Smith, 2012) and knowledge manipulation (Holsapple & Joshi, 2002; Kirmani & Zhu, 2007; Kirkbesoglu & Sagsan, 2009; Xu et al., 2011; Kim, 2014).

 

Recent research seeks further contributions, addressing cognitive processes relating to learning behaviour and the types of learning behaviour in the literature (Vellore et al., 1996; Grégoire et al., 2009; López-Ortega, 2013; Wrona et al., 2013). In addition, there is a calling need for research on two areas. Firstly, learning construct relating to quality (Asif et al., 2013; Li-An & Kuo, 2013; Makkonen & Inkinen, 2014), relevance (Beuchat, 2012; Saiz et al., 2012) and value (Nasiriyar & Nesta, 2013; Atapattu & Jayakody, 2014; Lecuona & Reitzig, 2014; Martelo-Landroguez & Cegarra-Navarro, 2014; Ye et al., 2014) Secondly, social collaboration in the digital workplace from traditional organisation workforce (Trier, 2008; Gnyawali et al., 2010; Hasgall, 2013; Sundararajan et al., 2013; Rhue & Sundararajan, 2014; Zhao et al., 2014). 

3.8. Condition 8: Knowledge Analytics

According to APQC’s research, the research conducted in 2013-2014 highlighted interviews from 15 organisations and 757 respondents. One of the key concerns is that many organisations will have a shortage of experts in technical, engineering, and organisation. This is an area where further research is required, and two areas are considered worth exploring. Firstly, knowledge analytics for knowledge mapping of key expert areas. APQC suggest knowledge map is considered to be a powerful tool for an organisation’s knowledge and critical for pinpointing areas deemed at risk. Therefore, articulating the creation of a knowledge map is suggested to reveal weak links and bottlenecks in the flow of knowledge. This area is yet to mature, and research is required (Lee & Fink, 2013; Hao et al., 2014; Irani et al., 2014). Secondly, APQC highlights that data and analytics will shape the knowledge management field (Tsai, 2013; Usman et al., 2013; Wang et al., 2013; Fisch et al., 2014; Natek & Zwilling, 2014; Tsui et al., 2014). In specific the following themes, are competitiveness and quality, the impact of knowledge analytics, and the role of knowledge in mining and memory. The emphasis of this research area focuses on the knowledge-centric approach (Liu et al., 2014). On the one hand, this research can support information systems, and on the other hand, it helps to provide designs and frameworks for information and knowledge management scholars. 

4. Critique of conditions

Over the past 35 years, knowledge management has permeated organisation activity. This growing use of knowledge transfer promised an unprecedented increase in innovation, creativity, and productivity, and yet, the promise is unfulfilled, due to a lack of understanding of knowledge behaviour. Knowledge transfer is linked directly to functionality, and ease of use, learning. Furthermore, technical systems lack the necessary tools to apply effectively what is known about knowledge transfer. Organisations are facing an increasing number of challenges focused on the recovery, processing and analysis of knowledge transfer and loss. From the above, systems and technical design need to expand their focus beyond the functional requirement to include the cognitive and behavioural needs of humans. Given the large, interdisciplinary body of research, literature suggests knowledge transfer is of importance, however, complex.

The problems of knowledge transfer, such as language, action, and presentation are well documented. The various stages of the knowledge transfer process, from early gathering to production can be very varied and pose significant challenges for those involved in making sense of knowledge artefacts. Both condition 1 and condition 2 represent a structural technique. Condition 3 and 4 looks at optimisation problems from an organisation perspective, whilst 5, 6 and 7 look at optimisation problems in systems and technology. As organisations move towards digital, organisations are increasingly growing on narratives in their systems, leaving a knowledge footprint which can be used to understand knowledge mapping links. Solutions to design technology and systems create support for organisation health to meet challenges they face in the 21st and 22nd centuries. However, in the extant literature, there are numerous examples of its misapplication, such as the decision support system expert system.

Whilst research is leading the way in research into knowledge analytics, modelling and methodology, organisations also have their part to play. The objective of condition 8 seeks to inform and network opportunities, whether for extracting actionable knowledge from various knowledge sources or for innovation. Condition 8 primarily suggests understanding digital knowledge and how the relevant human will use it, what is available, where to obtain and what is likely to be used. The research overall establishes to seek signal and knowledge processing behaviour, which will benefit both the organisation and workforce, giving them efficient and effective ways to use knowledge mapping on a day-to-day basis.

5. Conclusion

This recent socio-cognitive interest has been partly driven by facilitated access to knowledge transfer. There is a significant interest in analysing knowledge transfer for research and practical purposes. For example, in analysing knowledge transfer, organisations see the opportunity for targeting knowledge loss. In particular, the interest behind organisation activities is how to identify effectively. Furthermore, organisations could establish an early warning for knowledge loss that should help provide a timely response to interruptions in operations and financials. Also, the workforce seeks to use knowledge from diverse expertise areas to make a more informed, sound and comprehensive decision. When examining knowledge transfer, social systems and behavioural problems associated with social learning, three questions become crucial:

1. What are the agent knowledge transfer or behavioural problems?

2. What are the knowledge transfer causes of the behavioural problems?

3. How can the knowledge transfer causes be eliminated to solve the behavioural problems?

Many researchers have spent considerable time focusing on question one, with some attempts on question 2. Question 2 focuses on the storytelling phases due to inadequate knowledge transfer with capturing on systems. Recent research is seeking to find answers to questions. However, due to the switch focus, there is an incomplete understanding of question 2

REFERENCES

Abbate, T., Coppolino, R. & Schiavone, F. (2013) Linking Entities in Knowledge Transfer: The Innovation Intermediaries. Journal of the Knowledge Economy, Volume: 4, Issue: 3, Page(s): 233-243.

Ackermann, F. & Eden, C. (1994) Issues in computer and non-computer supported GDSSs. Decision Support Systems, Volume: 12, Issue: 4–5, Page(s): 381-390.

Aiken, M. & Carlisle, J. (1992) An automated idea consolidation tool for computer-supported cooperative work. Information & Management, Volume: 23, Issue: 6, Page(s): 373-382.

Alavi, M. & Leidner, D. E. (2001) Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, Volume: 25, Issue: 1, Page(s): 107-136.

Anonymous. (2008) Tackling organisational knowledge loss head-on at VDOT. Knowledge Management Review, Volume: 11, Issue: 4, Page(s): 4.

Asif, M., de Vries, H. J. & Ahmad, N. (2013) Knowledge creation through quality management. Total Quality Management & Business Excellence, Volume: 24, Issue: 5-6, Page(s): 664-677.

Atapattu, A. W. M. M. & Jayakody, J. A. S. K. (2014) The interaction effect of organisational practices and employee values on knowledge management (KM) success. Journal of Knowledge Management, Volume: 18, Issue: 2, Page(s): 307-328.

Barney, J. B. (1996) The Resource-Based Theory of the Firm. Organization Science, Volume: 7, Issue: 5, Page(s): 469-469.

Barney, J. B., Ketchen, D. J. & Wright, M. (2011) The Future of Resource-Based Theory: Revitalisation or Decline? Journal of Management, Volume: 37, Issue: 5, Page(s): 1299-1315.

Beliveau, J. (2012) Middle Managers’ Behavioral Exemplarity: A Sine qua non Condition for Knowledge Transfer. In: of editor of. Proceedings of the 13th European Conference on Knowledge Management, Vols 1 and 2, 2012. Volume: Issue: Page(s): 75-82.

Beuchat, G. (2012) Just-in-Time Knowledge as a way of Increasing Quality and Relevance of Ideas in Open Innovation (OI) programs. In: of editor of. Proceedings of the 9th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning, 2012. Volume: Issue: Page(s): 345-350.

Bontis, N. (2001) CKO wanted — evangelical skills necessary: a review of the Chief Knowledge Officer position. Knowledge and Process Management, Volume: 8, Issue: 1, Page(s): 29-38.

Bou-Llusar, J. C. & Segarra-Ciprés, M. (2006) Strategic knowledge transfer and its implications for competitive advantage: an integrative conceptual framework. Journal of Knowledge Management, Volume: 10, Issue: 4, Page(s): 100-112.

Cambra-Fierro, J., Florin, J., Perez, L. & Whitelock, J. (2011) Inter-firm market orientation as antecedent of knowledge transfer, innovation and value creation in networks. Management Decision, Volume: 49, Issue: 3, Page(s): 444-467.

Cattani, G., Dunbar, R. L. M. & Shapira, Z. (2012) Value Creation and Knowledge Loss: The Case of Cremonese Stringed Instruments. Organization Science, Volume: 24, Issue: 3, Page(s): 813-830.

Cegarra-Hialarro, J. G., Dewhurst, F. & Martins, B. (2007) Implementing knowledge management practices through chief knowledge officers. In: Remenyi, D., of editor of. Proceedings of the 8th European Conference on Knowledge Management, Vol 1 and 2, 2007, Nr Reading. Academic Conferences Ltd, Volume: Issue: Page(s): 200-205.

Çelen, B. & Hyndman, K. (2012) Social Learning Through Endogenous Information Acquisition: An Experiment. Management Science, Volume: 58, Issue: 8, Page(s): 1525-1548.

Chan, T. Y., Li, J. & Pierce, L. (2014) Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth. Marketing Science, Volume: Issue: Page(s).

Chang, Y. B. & Gurbaxani, V. (2012) Information Technology Outsourcing, Knowledge Transfer, and Firm Productivity: An Empirical Analysis. MIS Quarterly, Volume: 36, Issue: 4, Page(s): 1043.

Chen, F., Bapuji, H., Dyck, B. & Wang, X. (2012) I learned more than I taught: the hidden dimension of learning in intercultural knowledge transfer. The Learning Organization, Volume: 19, Issue: 2, Page(s): 109-120.

Chen, J. & McQueen, R. J. (2010) Knowledge transfer processes for different experience levels of knowledge recipients at an offshore technical support center. Information Technology & People, Volume: 23, Issue: 1, Page(s): 54-79.

Chen, Y., Ip, H. H. S., Li, S. & Wang, G. (2009) Discovering hidden knowledge in data classification via multivariate analysis. Expert Systems, Volume: 27, Issue: 2, Page(s): 90-100.

Conner, K. R. (1991) A Historical Comparison of Resource-Based Theory and Five Schools of Thought Within Industrial Organization Economics: Do We Have a New Theory of the Firm? Journal of Management, Volume: 17, Issue: 1, Page(s): 121-154.

Conner, K. R. & Prahalad, C. K. (1996) A Resource-Based Theory of the Firm: Knowledge Versus Opportunism. Organization Science, Volume: 7, Issue: 5, Page(s): 477-501.

Daghfous, A., Belkhodja, O. & Angell, L. C. (2013) Understanding and managing knowledge loss. Journal of Knowledge Management, Volume: 17, Issue: 5, Page(s): 639-660.

Das, T. K. & Teng, B. S. (2000) A Resource-Based Theory of Strategic Alliances. Journal of Management, Volume: 26, Issue: 1, Page(s): 31-61.

Earl, M. J. & Scott, I. A. (1999) Opinion: What is a Chief Knowledge Officer? Sloan Management Review, Volume: 40, Issue: 2, Page(s): 29-38.

Ekambaram, A. & Johansen, A. (2011) Managing Uncertainty in Projects - A Means to Knowledge Transfer, Learning and Organization Development. In: Lehner, F. & Bredl, K., of editor of. Proceedings of the 12th European Conference on Knowledge Management, Vols 1 and 2, 2011, Nr Reading. Academic Conferences Ltd, Volume: Issue: Page(s): 259-266.

FERN, M. & CARDINAL, L. B. (2003) FROM CORPORATE STRATEGY TO PERFORMANCE: THE EFFECT OF INTERUNIT KNOWLEDGE TRANSFER ON INNOVATIVENESS. Academy of Management Proceedings, Volume: 2003, Issue: 1, Page(s): A1-A6.

Fisch, D., Kalkowski, E. & Sick, B. (2014) Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications. Knowledge and Data Engineering, IEEE Transactions on, Volume: 26, Issue: 3, Page(s): 652-666.

Foss, N. J. (1996) Knowledge-Based Approaches to the Theory of the Firm: Some Critical Comments. Organization Science, Volume: 7, Issue: 5, Page(s): 470-476.

Francescato, D., Mebane, M., Porcelli, R., Attanasio, C. & Pulino, M. (2007) Developing professional skills and social capital through computer supported collaborative learning in university contexts. International Journal of Human-Computer Studies, Volume: 65, Issue: 2, Page(s): 140-152.

Gnyawali, D. R., Fan, W. & Penner, J. (2010) Competitive Actions and Dynamics in the Digital Age: An Empirical Investigation of Social Networking Firms. Information Systems Research, Volume: 21, Issue: 3, Page(s): 594-613.

Grant, R. M. (1991) The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation. California Management Review, Volume: 33, Issue: 3, Page(s): 114-135.

Grant, R. M. (1996) Toward a knowledge-based theory of the firm. Strategic Management Journal, Volume: 17, Issue: S2, Page(s): 109-122.

Greco, M., Grimaldi, M. & Hanandi, M. (2013) How to Select Knowledge Management Systems: A Framework to Support Managers. International Journal of Engineering Business Management, Volume: Issue: Page(s): 1.

Grégoire, D. A., Barr, P. S. & Shepherd, D. A. (2009) Cognitive Processes of Opportunity Recognition: The Role of Structural Alignment. Organization Science, Volume: 21, Issue: 2, Page(s): 413-431.

Guadamillas-Gómez, F. & Donate-Manzanares, M. J. (2011) Ethics and corporate social responsibility integrated into knowledge management and innovation technology. Journal of Management Development, Volume: 30, Issue: 6, Page(s): 569-581.

Guns, B. (1997) The Chief Knowledge Officers Role: Challenges and Competencies. Journal of Knowledge Management, Volume: 1, Issue: 4, Page(s).

Gutwin, C. (2005) Book Review: Public and Situated Displays: Social and Interactional Aspects of Shared Display Technologies, Kenton O’Hara, Mark Perry, Elizabeth Churchill and Daniel Russell (eds.), The Kluwer International Series on Computer Supported Cooperative Work, 2003, 456 pp. ISBN 1-4020-1677-8. Computer Supported Cooperative Work (CSCW), Volume: 14, Issue: 3, Page(s): 287-291.

Hao, J., Yan, Y., Gong, L., Wang, G. & Lin, J. (2014) Knowledge map-based method for domain knowledge browsing. Decision Support Systems, Volume: 61, Issue: 0, Page(s): 106-114.

Hasgall, A. (2013) Digital social networks as complex adaptive systems. Vine, Volume: 43, Issue: 1, Page(s): 78-95.

Hernández-Ramos, P. & Bowker, G. C. (2007) In Absentia: Designing for Social Learning. Computer Supported Cooperative Work (CSCW), Volume: 17, Issue: 1, Page(s): 87-90.

Hiltz, S. R., Diaz, P. & Mark, G. (2011) Introduction: Social Media and Collaborative Systems for Crisis Management. Acm Transactions on Computer-Human Interaction, Volume: 18, Issue: 4, Page(s).

Hinterhuber, H. (2007) Enhancing knowledge transfer in multinational corporations: a dynamic capabilities driven model. Knowledge Management Research & Practice, Volume: 5, Issue: 1, Page(s): 34-43.

Hoeben, A. & Stappers, P. J. (2005) Direct talkback in computer supported tools for the conceptual stage of design. Knowledge-Based Systems, Volume: 18, Issue: 8, Page(s): 407-413.

Holsapple, C. W. & Joshi, K. D. (2002) Knowledge manipulation activities: results of a Delphi study. Information & Management, Volume: 39, Issue: 6, Page(s): 477-490.

Howorth, C., Smith, S. M. & Parkinson, C. (2012) Social Learning and Social Entrepreneurship Education. Academy of Management Learning & Education, Volume: 11, Issue: 3, Page(s): 371-389.

Huang, J., Ling, J., Yang, J. & Zhao, Q. (2010) Key Success Factors in Knowledge Transfer during M&A in Traditional Industries: An Empirical Study. Journal of International Technology and Information Management, Volume: 19, Issue: 4, Page(s): 109-IV.

Huang, J., Ling, J., Yang, J. & Zhao, Q. (2012) Key Successful Factors in Knowledge Transfer during M&A in Traditional Industries: An Empirical Study. Journal of International Technology and Information Management, Volume: 21, Issue: 4, Page(s): 43-65.

Huerta, E., Salter, S. B., Lewis, P. A. & Yeow, P. (2012) Motivating Employees to Share Their Failures in Knowledge Management Systems: Anonymity and Culture. Journal of Information Systems, Volume: 26, Issue: 2, Page(s): 93-117.

Irani, Z., Sharif, A., Kamal, M. M. & Love, P. E. D. (2014) Visualising a knowledge mapping of information systems investment evaluation. Expert Syst Appl, Volume: 41, Issue: 1, Page(s): 105-125.

Jiang, M. S. & Aulakh, P. S. (2013) Antecedents and Performance Implications of Managerial Knowledge Transfer in International Licensing. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Joe, C., Yoong, P. & Patel, K. (2013) Knowledge loss when older experts leave knowledge-intensive organisations. Journal of Knowledge Management, Volume: 17, Issue: 6, Page(s): 913-927.

Joia, L. A. & Lemos, B. (2010) Relevant factors for tacit knowledge transfer within organisations. Journal of Knowledge Management, Volume: 14, Issue: 3, Page(s): 410-427.

Joshi, K. D., Sarker, S. & Sarker, S. (2007) Knowledge transfer within information systems development teams: Examining the role of knowledge source attributes. Emerging Issues in Collaborative Commerce, Volume: 43, Issue: 2, Page(s): 322-335.

Kaplan, B. (2007) CREATING LONG-TERM VALUE AS CHIEF KNOWLEDGE OFFICER. Knowledge Management Review, Volume: 10, Issue: 4, Page(s): 30-33.

Khriss, I., Keller, R. K. & Hamid, I. A. (2000) Pattern-based refinement schemas for design knowledge transfer. Knowledge-Based Systems, Volume: 13, Issue: 6, Page(s): 403-415.

Kim, J. S. (2014) Development of a Composite Knowledge Manipulation Tool: K-Expert. Expert Syst Appl, Volume: 41, Issue: 9, Page(s): 4337-4348.

Kirkbesoglu, E. & Sagsan, M. (2009) Knowledge Manipulation in Inter Organizational Networks: a new Role for Brokers. In: Bolisani, E. & Scarso, E., of editor of. Proceedings of the 10th European Conference on Knowledge Management , Vols 1 and 2, 2009, Nr Reading. Academic Conferences Ltd, Volume: Issue: Page(s): 452-457.

Kirmani, A. & Zhu, R. (2007) Vigilant against Manipulation: The Effect of Regulatory Focus on the Use of Persuasion Knowledge. Journal of Marketing Research, Volume: 44, Issue: 4, Page(s): 688-701.

Lahtinen, J. (2013) Local social knowledge management: A case study of social learning and knowledge sharing across organisational boundaries. Journal of Information Science, Volume: 39, Issue: 5, Page(s): 661-675.

Lecuona, J. R. & Reitzig, M. (2014) Knowledge worth having in ‘excess’: The value of tacit and firm-specific human resource slack. Strategic Management Journal, Volume: 35, Issue: 7, Page(s): 954-973.

Lee, J. & Fink, D. (2013) Knowledge mapping: encouragements and impediments to adoption. Journal of Knowledge Management, Volume: 17, Issue: 1, Page(s): 16-28.

Lee, J. Y. & Bell, D. R. (2013) Neighborhood Social Capital and Social Learning for Experience Attributes of Products. Marketing Science, Volume: 32, Issue: 6, Page(s): 960-976.

Letmathe, P., Schweitzer, M. & Zielinski, M. (2012) How to learn new tasks: Shop floor performance effects of knowledge transfer and performance feedback. Journal of Operations Management, Volume: 30, Issue: 3, Page(s): 221-236.

Li-An, H. & Kuo, T.-H. (2013) How system quality and incentive affect knowledge sharing. Industrial Management + Data Systems, Volume: 113, Issue: 7, Page(s): 1048-1063.

Li, C.-Y. (2012) Knowledge stickiness in the buyer–supplier knowledge transfer process: The moderating effects of learning capability and social embeddedness. Expert Syst Appl, Volume: 39, Issue: 5, Page(s): 5396-5408.

Liao, H., Liu, D. & Loi, R. (2010) Looking at Both Sides of the Social Exchange Coin: A Social Cognitive Perspective on the Joint Effects of Relationship Quality and Differentiation on Creativity. Academy of Management Journal, Volume: 53, Issue: 5, Page(s): 1090-1109.

Lin, C., Frank Chen, F., Wan, H.-d., Min Chen, Y. & Kuriger, G. (2013) Continuous improvement of knowledge management systems using Six Sigma methodology. Robotics and Computer-Integrated Manufacturing, Volume: 29, Issue: 3, Page(s): 95-103.

Lin, T.-C. & Huang, C.-C. (2010) Withholding effort in knowledge contribution: The role of social exchange and social cognitive on project teams. Information & Management, Volume: 47, Issue: 3, Page(s): 188-196.

Liu, L., Caldwell, B. S., Wang, H. & Li, Y. (2014) A knowledge-centric CNC machine tool design and development process management framework. International Journal of Production Research, Volume: 52, Issue: 20, Page(s): 6033-6051.

López-Ortega, O. (2013) Computer-assisted creativity: Emulation of cognitive processes on a multi-agent system. Expert Syst Appl, Volume: 40, Issue: 9, Page(s): 3459-3470.

Lorenzo, O., Kawalek, P. & Ramdani, B. (2012) Enterprise applications diffusion within organisations: A social learning perspective. Information & Management, Volume: 49, Issue: 1, Page(s): 47-57.

Maden, C., Ozcelik, H. & Karacay, G. (2013) Understanding Employees’ Responses to Unmet Career Expectations: A Social Cognitive Theory Approach. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Makkonen, T. & Inkinen, T. (2014) Innovation quality in knowledge cities: Empirical evidence of innovation award competitions in Finland. Expert Syst Appl, Volume: 41, Issue: 12, Page(s): 5597-5604.

Malhotra, Y. (2004) Why Knowledge Management Systems Fail: Enablers and Constraints of Knowledge Management in Human Enterprises. In: Holsapple, C., of editor of. Handbook on Knowledge Management 1. Page(s): 577-599. Springer Berlin Heidelberg.

Mantei, M. (1989) Observation of executives using a computer supported meeting environment. Decision Support Systems, Volume: 5, Issue: 2, Page(s): 153-166.

Martelo-Landroguez, S. & Cegarra-Navarro, J.-G. (2014) Linking knowledge corridors to customer value through knowledge processes. Journal of Knowledge Management, Volume: 18, Issue: 2, Page(s): 342-365.

Maurer, I., Bartsch, V. & Ebers, M. (2011) The Value of Intra-organizational Social Capital: How it Fosters Knowledge Transfer, Innovation Performance, and Growth. Organization Studies, Volume: 32, Issue: 2, Page(s): 157-185.

McLaughlin, S. (2010) Six tenets for developing an effective knowledge transfer strategy. Vine, Volume: 40, Issue: 2, Page(s): 153-182.

Moffett, S. & Patterson, G. (1998) Enabling Computer Supported Collaborative Work (CSCW) within Distributed Virtual Environments. In:  International Conference on Industry, Engineering and Management Systems, 1998, Florida, USA. Volume: Issue: Page(s): 222-226.

Morey, D. (2001) High-speed knowledge management: integrating operations theory and knowledge management for rapid results. Journal of Knowledge Management, Volume: 5, Issue: 4, Page(s): 322-328.

Nasiriyar, M. & Nesta, L. J. (2013) Investigating Understudied Moderators in Meta-Analysis

Value-generating Properties of Knowledge Base andReturn and Flexibility in Knowledge Integration. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Natek, S. & Zwilling, M. (2014) Student data mining solution–knowledge management system related to higher education institutions. Expert Syst Appl, Volume: 41, Issue: 14, Page(s): 6400-6407.

Nickerson, J. A. & Zenger, T. R. (2004) A Knowledge-Based Theory of the Firm—The Problem-Solving Perspective. Organization Science, Volume: 15, Issue: 6, Page(s): 617-632.

Nicotra, M., Romano, M. & Giudice, M. (2013) The Evolution Dynamic of a Cluster Knowledge Network: the Role of Firms’ Absorptive Capacity. Journal of the Knowledge Economy, Volume: 5, Issue: 1, Page(s): 70-93.

Nonaka, I., Kodama, M., Hirose, A. & Kohlbacher, F. (2014) Dynamic fractal organisations for promoting knowledge-based transformation – A new paradigm for organisational theory. European Management Journal, Volume: 32, Issue: 1, Page(s): 137-146.

Nowak, R. (2013) Enhancing Organisational Knowledge: The Antecedents of Potential and Realised Absorptive Capacity. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

O’Dell, C. & Grayson Jr, C. J. (1999) Knowledge transfer: Discover your value proposition. Strategy & Leadership, Volume: 27, Issue: 2, Page(s): 10-15.

Oberschmidt, K. (2013) Team Organizational Citizenship Behavior’s Impact on Absorptive Capacity and New Product Success. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Ofstein, L. F., Shrader, R. C. & Renko, M. (2013) Innovating in the Entrepreneurial Firm: Boundary Spanning Activities and Absorptive Capacity. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Osterloh, M. & Frey, B. S. (2000) Motivation, Knowledge Transfer, and Organisational Forms. Organization Science, Volume: 11, Issue: 5, Page(s): 538-550.

Palacios-Marqués, D., Peris-Ortiz, M. & Merigó, J. M. (2013) The effect of knowledge transfer on firm performance: An empirical study in knowledge-intensive industries. Management Decision, Volume: 51, Issue: 5, Page(s): 973-985.

Parent, R., Roy, M. & St-Jacques, D. (2007) A systems-based dynamic knowledge transfer capacity model. Journal of Knowledge Management, Volume: 11, Issue: 6, Page(s): 81-93.

Parsons, S. (1995) Artifical intelligence through search by Chris Thornton and Benedict du Boulay, Intellect, 1992, pp 363, ISBN 1-871516-24-2.The engineering of knowledge-based systems–theory and practice by Avelino J. Gonzalez and Doughlas D. Dankel, Prentice Hall International, 1993, pp 523, ISBN 0-13-334293-X. The Knowledge Engineering Review, Volume: 10, Issue: 01, Page(s): 106-108.

Paton, R. A. & McLaughlin, S. (2008) Services innovation:: Knowledge transfer and the supply chain. European Management Journal, Volume: 26, Issue: 2, Page(s): 77-83.

Patriotta, G., Castellano, A. & Wright, M. (2013) Coordinating knowledge transfer: Global managers as higher-level intermediaries. Journal of World Business, Volume: 48, Issue: 4, Page(s): 515-526.

Pawlowski, J. M., Bick, M., Peinl, R., Thalmann, S., Maier, R., Hetmank, L., Kruse, P., Martensen, M. & Pirkkalainen, H. (2014) Social Knowledge Environments. Business & Information Systems Engineering, Volume: 6, Issue: 2, Page(s): 81-88.

Petrovic, O. & Krickl, O. (1994) Traditionally-moderated versus computer supported brainstorming: a comparative study. Information & Management, Volume: 27, Issue: 4, Page(s): 233-243.

Posen, H. E. & Chen, J. S. (2013) An Advantage of Newness: Vicarious Learning Despite Limited Absorptive Capacity. Organization Science, Volume: 24, Issue: 6, Page(s): 1701-1716.

Reihlen, M. & Ringberg, T. (2013) Uncertainty, pluralism, and the knowledge-based theory of the firm: From J.-C. Spender’s contribution to a socio-cognitive approach. European Management Journal, Volume: 31, Issue: 6, Page(s): 706-716.

Rhue, L. & Sundararajan, A. (2014) Digital access, political networks and the diffusion of democracy. Social Networks, Volume: 36, Issue: 0, Page(s): 40-53.

Riss, U. V., Cress, U., Kimmerle, J. & Martin, S. (2007) Knowledge transfer by sharing task templates: two approaches and their psychological requirements. Knowledge Management Research & Practice, Volume: 5, Issue: 4, Page(s): 287-296.

Roberts, P. W. & Sterling, A. D. (2012) Network Progeny? Prefounding Social Ties and the Success of New Entrants. Management Science, Volume: 58, Issue: 7, Page(s): 1292-1304.

Rodrigues, P. & de Oliveira, E. R. (2010) The Importance of Knowledge Management in Corporate Social Responsibility and the Effects on the Consumers - Presenting the Case of Cafes Delta. In: Tome, E., of editor of. Proceedings of the 11th European Conference on Knowledge Management, Vols 1 and 2, 2010, Nr Reading. Academic Conferences Ltd, Volume: Issue: Page(s): 830-835.

Saiz, L., Manzanedo, A., Alcalde, R. & Alonso, M. (2012) An Empirical and Theoretical Study of the Relevance of Knowledge Management in Energy Power Generator Organisations. In: of editor of. Proceedings of the 13th European Conference on Knowledge Management, Vols 1 and 2, 2012. Volume: Issue: Page(s): 1033-1040.

Salleh, K., Chong, S. C., Syed Ahmad, S. N. & Syed Ikhsan, S. O. S. (2012) Learning and knowledge transfer performance among public sector accountants: an empirical survey. Knowledge Management Research & Practice, Volume: 10, Issue: 2, Page(s): 164-174.

Sampson, D. G. & Zervas, P. (2013) Learning object repositories as knowledge management systems. Knowledge Management & E-Learning, Volume: 5, Issue: 2, Page(s): 117-136.

Sankowska, A. (2013) Relationships between organisational trust, knowledge transfer, knowledge creation, and firm's innovativeness. Learning Organization, The, Volume: 20, Issue: 1, Page(s): 85-100.

Sha, X., Ting-Ting Chang, K., Zhang, C. & Zhang, C. (2013) Knowledge popularity in a heterogeneous network: Exploiting the contextual effects of document popularity in knowledge management systems. Journal of the American Society for Information Science and Technology, Volume: 64, Issue: 9, Page(s): 1842-1851.

Sheng, M. L., Chang, S.-Y., Teo, T. & Lin, Y.-F. (2013) Knowledge barriers, knowledge transfer, and innovation competitive advantage in healthcare settings. Management Decision, Volume: 51, Issue: 3, Page(s): 461-478.

Shin, S. K. & Kook, W. (2014) Can knowledge be more accessible in a virtual network?: Collective dynamics of knowledge transfer in a virtual knowledge organisation network. Decision Support Systems, Volume: 59, Issue: 0, Page(s): 180-189.

Shriver, S. K., Nair, H. S. & Hofstetter, R. (2013) Social Ties and User-Generated Content: Evidence from an Online Social Network. Management Science, Volume: 59, Issue: 6, Page(s): 1425-1443.

Siachou, E. & Ioannidis, A. (2010) Knowledge Transfer in Strategic Alliances: Moderating Effects of Limited Absorptive Capacity and Powerful Relationships on Business Model Innovation Performance. In: Tome, E., of editor of. Proceedings of the 11th European Conference on Knowledge Management, Vols 1 and 2, 2010, Nr Reading. Academic Conferences Ltd, Volume: Issue: Page(s): 933-943.

Skvoretz, J. (2013) Diversity, Integration, and Social Ties: Attraction versus Repulsion as Drivers of Intra- and Intergroup Relations. American Journal of Sociology, Volume: 119, Issue: 2, Page(s): 486-517.

Skyrme, D. J. (1995) Groupware in the 21st century: Computer supported co-operative working toward the millennium: Edited by Peter Lloyd FRSA, Adamantine Press (1994), 304 pp., £19.95 (paper), £45.00 (hardback). Long Range Planning, Volume: 28, Issue: 1, Page(s): 122-123.

Smith, S. P. (2012) Towards a Knowledge Management Methodology for Articulating the Role of Hidden Knowledges. Doctor of Philosophy Thesis, University of Oxford, Oxford, England, UK.

Song, J., Almeida, P. & Wu, G. (2003) Learning–by–Hiring: When Is Mobility More Likely to Facilitate Interfirm Knowledge Transfer? Management Science, Volume: 49, Issue: 4, Page(s): 351-365.

Soo, C., Tian, A. W. & Cordery, J. (2013) The Role of Intellectual Capital-Enhancing HR in Organisational Absorptive Capacity and Innovation. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Spender, J. C. (1998) Pluralist Epistemology and the Knowledge-Based Theory of the Firm. Organisation, Volume: 5, Issue: 2, Page(s): 233-256.

Spender, J. C. (2003) Exploring uncertainty and emotion in the knowledge-based theory of the firm. Information Technology & People, Volume: 16, Issue: 3, Page(s): 266-288.

Steenrod, J. E. & Lin, M. K. (2013) The Environmental Context of Absorptive Capacity for Collaborative Care Management. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Stritch, J. M. & Christensen, R. K. (2013) “Social Learning, Parental Influence, and Public Service Orientations in Undergraduate Students”. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Sun, S. & Lin, Z. (2003) Models and techniques of computer supported cooperative conceptual design. Integrated Manufacturing Systems, Volume: 14, Issue: 6, Page(s): 530-536.

Sundararajan, A., Provost, F., Oestreicher-Singer, G. & Aral, S. (2013) Research Commentary—Information in Digital, Economic, and Social Networks. Information Systems Research, Volume: 24, Issue: 4, Page(s): 883-905.

Sutanto, J. & Jiang, Q. (2013) Knowledge seekers’ and contributors’ reactions to recommendation mechanisms in knowledge management systems. Information & Management, Volume: 50, Issue: 5, Page(s): 258-263.

Sveiby, K.-E. (2001) A knowledge-based theory of the firm to guide in strategy formulation. Journal of Intellectual Capital, Volume: 2, Issue: 4, Page(s): 344-358.

Tortoriello, M. (2014) The social underpinnings of absorptive capacity: The moderating effects of structural holes on innovation generation based on external knowledge. Strategic Management Journal, Volume: Issue: Page(s): n/a-n/a.

Tortoriello, M., Moebus, S. & Täube, F. A. (2013) Lost in transition: How network structure affects knowledge loss between individuals. Academy of Management Proceedings, Volume: 2013, Issue: 1, Page(s).

Trapido, D. (2012) Dual Signals: How Competition Makes or Breaks Interfirm Social Ties. Organization Science, Volume: 24, Issue: 2, Page(s): 498-512.

Trier, M. (2008) Research Note—Towards Dynamic Visualization for Understanding Evolution of Digital Communication Networks. Information Systems Research, Volume: 19, Issue: 3, Page(s): 335-350.

Tsai, H.-H. (2013) Knowledge management vs. data mining: Research trend, forecast and citation approach. Expert Syst Appl, Volume: 40, Issue: 8, Page(s): 3160-3173.

Tsai, W. (2001) Knowledge Transfer in Intraorganizational Networks: Effects of Network Position and Absorptive Capacity on Business Unit Innovation and Performance. Academy of Management Journal, Volume: 44, Issue: 5, Page(s): 996-1004.

Tsui, E., Wang, W. M., Cai, L., Cheung, C. F. & Lee, W. B. (2014) Knowledge-based extraction of intellectual capital-related information from unstructured data. Expert Syst Appl, Volume: 41, Issue: 4, Part 1, Page(s): 1315-1325.

Usman, M., Pears, R. & Fong, A. C. M. (2013) A data mining approach to knowledge discovery from multidimensional cube structures. Knowledge-Based Systems, Volume: 40, Issue: Page(s): 36-49.

Van Witteloostuijn, A. & Boone, C. (2006) A Resource-Based Theory of Market Structure and Organizational Form. Academy of Management Review, Volume: 31, Issue: 2, Page(s): 409-426.

Vellore, R. C., Sen, A. & Vinze, A. S. (1996) Use of cognitive process modeling in knowledge base systems integration. Decision Support Systems, Volume: 16, Issue: 4, Page(s): 359-392.

Vijayakumar, V., Gey, R. & Wende, E. (2008) Storytelling - A method to start knowledge transfer in offshore software development teams - Research in progress paper. In: Harorimana, D. & Watkins, D., of editor of. Proceedings of the 9th European Conference on Knowledge Management, 2008, Nr Reading. Academic Conferences Ltd, Volume: Issue: Page(s): 913-921.

Wan, W. P., Hoskisson, R. E., Short, J. C. & Yiu, D. W. (2010) Resource-Based Theory and Corporate Diversification: Accomplishments and Opportunities. Journal of Management, Volume: 37, Issue: 5, Page(s): 1335-1368.

Wang, Y., Meister, D. B. & Gray, P. H. (2013) Social Influence and Knowledge Management Systems Use: Evidence from Panel Data. MIS Quarterly, Volume: 37, Issue: 1, Page(s): 299-313.

Warnier, V., Weppe, X. & Lecocq, X. (2013) Extending resource-based theory: considering strategic, ordinary and junk resources. Management Decision, Volume: 51, Issue: 7, Page(s): 1359-1379.

Wei, K.-K., Teo, H.-H., Chan, H. C. & Tan, B. C. Y. (2010) Conceptualising and Testing a Social Cognitive Model of the Digital Divide. Information Systems Research, Volume: 22, Issue: 1, Page(s): 170-187.

Wilkesmann, M. & Wilkesmann, U. (2011) Knowledge transfer as interaction between experts and novices supported by technology. Vine, Volume: 41, Issue: 2, Page(s): 96-112.

Wrona, T., Ladwig, T. & Gunnesch, M. (2013) Socio-cognitive processes in strategy formation – A conceptual framework. European Management Journal, Volume: 31, Issue: 6, Page(s): 697-705.

Wu, W. L., Hsu, B. F. & Yeh, R. S. (2007) Fostering the determinants of knowledge transfer: a team-level analysis. Journal of Information Science, Volume: 33, Issue: 3, Page(s): 326-339.

Xu, J. (2005) Mining static and dynamic structural patterns in networks for knowledge management: A computational framework and case studies. 3168596 Ph.D., The University of Arizona, Ann Arbor.

Xu, Y., Xu, Z., Jiang, X. & Scott, P. (2011) Developing a knowledge-based system for complex geometrical product specification (GPS) data manipulation. Knowledge-Based Systems, Volume: 24, Issue: 1, Page(s): 10-22.

Ye, M., Wu, X., Hu, X. & Hu, D. (2014) Knowledge reduction for decision tables with attribute value taxonomies. Knowledge-Based Systems, Volume: 56, Issue: Page(s): 68-78.

Zeng, X. & Wei, L. (2012) Social Ties and User Content Generation: Evidence from Flickr. Information Systems Research, Volume: 24, Issue: 1, Page(s): 71-87.

Zhao, Y. C., Xu, X., Sun, X. & Zhu, Q. (2014) An integrated framework of online generative capability: interview from digital immigrants. Aslib Journal of Information Management, Volume: 66, Issue: 2, Page(s): 219-239.

Zhelyazkov, P. I. (2012) When Does the Glue of Social Ties Dissolve? Academy of Management Proceedings, Volume: 2012, Issue: 1, Page(s): 1-1.

Zhuang, H., Tang, J., Tang, W., Lou, T., Chin, A. & Wang, X. (2012) Actively learning to infer social ties. Data Mining and Knowledge Discovery, Volume: 25, Issue: 2, Page(s): 270-297.