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Knowledge management is crucial for enterprise resource planning (ERP) systems implementation in real industrial environments, but this is a highly demanding task. The purpose of this paper is to examine the effectiveness of knowledge identification, categorisation and prioritisation that contributes to achieving ERP implementation success.

This study adopts a mixed methods approach; a qualitative phase to identify and categorise knowledge types and sub-types; conducting in-depth interviews with ERP clients and implementation partners; plus a quantitative phase to prioritise knowledge types and sub-types based on their contribution to achieving ERP success for business performance improvement. An analytic hierarchy process-based questionnaire was used to collect empirical data for the quantitative phase.

This study has been able to identify, categorise and rank various types of ERP-related knowledge based on in-depth interviews and survey responses from both ERP clients and implementation partners. In total, 4 knowledge types and 21 sub-types were ranked based on their contribution to achieving ERP success; 4 variables of information quality, systems quality, individual impact and organisational impact were used to measure ERP success.

The empirical findings demonstrate exactly what kinds of knowledge need to be managed, enabling knowledge prioritisation when a client organisation or an implementation partner steps into an ERP implementation, in a real industrial environment.

This paper aims at investigating the multifaceted nature of innovation networks by focusing on two research questions: Do cluster actors exchange only one type of innovation-related knowledge? Do cluster actors play different roles in innovation-related knowledge exchange?

This paper builds on data collected at the firm level in an Italian aerospace cluster, that is a technology-intensive industry where innovation is at the base of local competitiveness. A questionnaire was used to collect both attribute data and relational data concerning collaboration and the flows of knowledge in innovation networks. The authors distinguished among three types of knowledge (technological, managerial and market knowledge) and five types of brokerage roles (coordinator, gatekeeper, liaison, representative and consultant). Data analysis relied on social network analysis techniques and software.

Concerning the first research question, the findings show that different types of knowledge flow in different ways in innovation networks. The different types of knowledge are unevenly exchanged. The exchange of technological knowledge is open to everyone in the cluster. The exchange of market and managerial knowledge is selective. Concerning the second research question, the authors suggest that different types of cluster actors (large firms, small- and medium-sized enterprises, research centers and universities and institutions for collaboration) do play different roles in innovation networks, especially with reference to the three types of knowledge considered in this study.

The present paper has some limitations. First of all, the analysis focuses on just one cluster (one industry in one specific location), cross- and comparative analyses with other clusters may illuminate the findings better, eliminating industry and geographical biases. Second, the paper focuses only on innovation-related knowledge exchanges within the cluster and not across it.

The results have practical implications both for policy makers and for managers. First, this research stresses how innovation often originates from a combination of different knowledge types acquired through the collaboration with heterogeneous cluster actors. Further, the analysis of brokerage roles in innovation-driven collaborations may help policy makers in designing programs for knowledge-transfer partnerships among the various actors of a cluster.

The paper suggests a clear need of developing professional figures capable of operating at the interface of different knowledge domains.

The data illuminate several aspects of how innovation takes place in a cluster opening up intriguing aspects that have been overlooked by extant literature. The authors believe that this may trigger several lines of further research on the topic.

This paper aims to present findings from a study of non-profit organizations (NPOs), including a model of knowledge needs that can be applied by practitioners and scholars to further develop the NPO sector.

A survey was conducted with NPOs operating in Canada and Australia. An analysis of survey responses identified the different types of knowledge essential for each organization. Respondents identified the importance of three pre-determined themes (quantitative data) related to knowledge needs, as well as a fourth option, which was a free text box (qualitative data). The quantitative and qualitative data were analyzed using descriptive statistical analyses and a grounded theory approach, respectively.

Analysis of the quantitative data indicates that NPOs ' needs are comparable in both countries. Analysis of qualitative data identified five major categories and multiple sub-categories representing the types of knowledge needs of NPOs. Major categories are knowledge about management and organizational practices, knowledge about resources, community knowledge, sectoral knowledge and situated knowledge. The paper discusses the results using semantic proximity and presents an emergent, evidence-based knowledge management (KM)-NPO model.

The findings contribute to the growing body of literature in the KM domain, and in the understudied research domain related to the knowledge needs and experiences of NPOs. NPOs will find the identified categories and sub-categories useful to undertake KM initiatives within their individual organizations. The study is also unique, as it includes data from two countries, Canada and Australia.

In universities, an important part of intellectual capital is the research‐ development‐transfer capital (R&D&T capital), due to the process of creation of scientific and technical knowledge and its transfer to the social environment. The aim of this paper is to identify the types of knowledge that act as drivers of R&D&T capital in a public university. To this end, first key knowledge types in reference to the strategic objectives of the university are identified, through both examination of documents concerned with the objectives and plans of the university and meetings with leading officers of it. Second, the activities involved in the process have been analysed through in‐depth personal interviews with a group of heads of research teams at the university. The results of the interviews have received both qualitative and statistical analyses. Subsequently, a definitive list of key types of knowledge as drivers of university R&D&T capital is also presented.

This paper aims to focus on how different types of knowledge are exchanged within innovation networks in the German energy industry. External factors such as market pressure through liberalization, de-carbonization and decentralization challenge established actors in the industry. Answers to these challenges cannot be found by single actors but require networks to gather and concentrate innovation activities. This implies a need for knowledge transfer among energy providers. The authors aim at exploring knowledge exchange relations in-depth by treating them as multidimensional flows which can comprise technological, market, managerial or regulatory knowledge. In detail, the authors examine patterns of knowledge exchange on network-, dyad- and firm-level. Furthermore, first, empiric results are provided on how two of these patterns, namely, a firm’s propensity to form multiplex instead of uniplex ties as well as the composition of externally acquired knowledge concerning the four types, influence organizational innovativeness.

The authors address their research questions by conducting an in-depth investigation of the largest network of municipal utilities in Germany. The analysis is based on quantitative data collected via standardized online questionnaires drawing on socio-metric methods to reconstruct knowledge exchange networks as well as traditional approaches from socio-empiric research to evaluate firm innovativeness.

The findings indicate that while technological, market, managerial and regulatory knowledge represent different types of knowledge with different exchange patterns, these transfers are interdependent. In particular, the analysis reveals non-hierarchical relations of complementarity. The authors furthermore provide evidence for the existence of ideal profiles for attaining different types of innovation. One central tendency across all of these profiles is that outperformers acquire regulatory knowledge to a significantly lesser degree than other firms and focus more on the other types instead.

This paper solely focusses on the largest network of municipal utilities whereby it is questionable how representative it is for the whole industry. Additionally, due to the cross-sectional design, the paper cannot fully rule out issues of endogeneity in the quantitative analysis.

This paper delivers valuable insights for managers in the energy sector who seek to either enter and manage inter-organizational networks or apply external knowledge to foster innovation. In particular, the authors reveal benchmark profiles for external knowledge acquisition which may serve as templates for strategic collaboration and innovation management.

To the authors’ knowledge, this paper presents the first innovation-related network analysis in the energy industry. Rather than operationalizing knowledge transfer as a simplex flow relation, the authors examine different types of knowledge, their patterns of exchange and their distinct effects on process, product and administrative innovations.

The purpose of this paper is to analyze how the internationalizing firm accumulates knowledge from different domains throughout time, during entry and post-entry moves in a foreign market. This paper focuses on market, institutional, internationalization and technological knowledge.

The paper uses a comparative case study method, relying on three longitudinal cases of multinational firms' (MNEs) internationalization processes, particularly concerning MNEs' entry and post-entry moves in one foreign market (Brazil).

Throughout the internationalization processes, the internationalizing firms unevenly developed experiential knowledge within Brazil. As a result, the market, internationalization, institutional and technological knowledge followed different accumulation patterns, distinguished based on precedence, simultaneity and speed. More specifically, (1) the market and institutional knowledge trajectories evolved simultaneously; (2) the market knowledge trajectory preceded that of technological knowledge and (3) the accumulation of internationalization knowledge happened faster than that of the market, institutional and technological knowledge.

This paper shows how knowledge accumulation in internationalization processes varies throughout time and according to the type of knowledge involved. While most of the literature relies on cross-sectional studies that discount the changing nature of knowledge, this paper shows that the internationalizing firm accumulates market, internationalization, institutional and technological knowledge following three temporal dimensions: precedence, simultaneity and speed.

The purpose of the paper is to examine the underlying components of information technology (IT) that support different models of knowledge management (KM).

This empirical study is conducted in the management consulting industry to examine the important link between IT and KM. Based on previous research, four knowledge models were developed for the management consulting industry based on the knowledge type and service type. Data collected through a survey from 115 management consulting firms in the USA and Canada were analyzed.

Regardless of the type of KM model utilized, the most widely used IT by management consulting firms was the internet‐related technology (e‐mail, internet, and search engine). The second important IT component was data management technology (document management, data warehousing, data mining, knowledge repositories, and database management). The third important IT was collaborating technology (videoconferencing, workflow management, groupware, group decision support systems, and knowledge maps). The least important IT was artificial intelligence (expert systems, case‐based reasoning systems, intelligent agent, and neural network).

This paper develops a new topology of KM models based on the knowledge type (exploitive and explorative) and service type (standardized and customized). Thus, four KM models are developed: reuser (exploitive/standardized); stabilizer (exploitive/customized); explorer (explorative/standardized); and innovator (explorative/customized). While IT has been widely accepted as an enabler for KM, its application for a different focus of KM has not been explored.

The purpose of this paper is to explore how an individual's cognitive style influences the type of knowledge they prefer to work with, and to identify how this relationship influences knowledge management strategies and their outcomes.

The paper integrates adaption‐innovation theory and aspects of knowledge management theories.

Adaptors are likely to prefer to work with knowledge that is relatively more explicit and innovators are likely to prefer to work with knowledge that is relatively more tacit. Understanding these preferences, and making the appropriate type of knowledge available to the right mix of adaptor and innovator types of individuals may influence organizational performance.

Conceptual and empirical research should consider how individuals' cognitive style influences their ability to utilize organizational knowledge resources.

Organizations should consider evaluating the cognitive style of their members in order to be able to better assign them to knowledge tasks. Group tasks should be planned with the mix of individuals' cognitive style in mind. These efforts should help to avoid underutilization of appropriate knowledge as well as overuse of inappropriate knowledge.

The paper proposes that cognitive style influences the degree to which an individual prefers to work with tacit or explicit knowledge. This preference can influence the type and degree of knowledge use when performing organizational tasks.

This paper aims to focus on the relationships between the levels of knowledge and the type of knowledge transfer approaches, and the relationships between the types of knowledge and the knowledge transfer approaches which were adopted in a study of knowledge transfer from a US‐based technical support center to an offshore support center in China.

The research was conducted as an interpretive case study. Three techniques (i.e. document review, participant observation, and semi‐structured interviews) were employed for data collection in the field.

The findings indicate that the lower the level of recipient absorptive and retentive capacity, the more difficulty the recipient will have in acquiring tacit and complex types of knowledge, and the more formal structured knowledge transfer approach the recipient will need to adopt. The results identify that “structured transfer stages” was used by novices to transfer embrained and encoded knowledge, while “unstructured copy” was widely adopted by advanced beginners to transfer encoded and embodied knowledge, “unstructured adaptation” was mainly utilized by those at the competence level to transfer embodied and embedded knowledge, and “unstructured fusion” was preferred by recipients at the proficiency level to transfer embodied and embedded knowledge as well.

The findings contribute to an understanding of the knowledge transfer processes required when US‐based firms outsource business processes to offshore countries with significantly different cultural contexts. The findings also reflect the testing of possible analytical structures for understanding the processes of knowledge transfer, and the mechanisms for knowledge transfer in a cross‐cultural business context.

The paper provides new insights into the knowledge transfer process for different levels of knowledge acquisition in a cross‐cultural business context.

Little is known, about the role played by start-ups in open innovation networks. Start-ups – due to their nature of new and emerging companies – can largely benefit from the knowledge that can flow intentionally or unintentionally from external partners during open innovation practices. When open innovation networks are not set among peers on both sides the authors expect to have more unintended knowledge flows. Such knowledge “leaks” – as the authors named them – in open innovation networks are totally unexplored in literature. Hence, the purpose of this paper is to focus “whether and how knowledge leaks occur in open innovation networks with start-ups”.

The research design of this study relies on social network analysis methods and techniques to disentangle the role of start-ups in open innovation networks – in a major Italian aerospace cluster – vis-à-vis the three types of knowledge considered in this study. Then the authors confirmed knowledge leaks to occur through a multiplexity analysis. In the second stage of the research, the authors decided to strengthen the results, making them more vivid and thorough, relying on four case studies.

The paper sheds light on a totally unexplored phenomenon, theorizing on the role of start-ups in open innovation networks and suggesting intriguing implications both for theory and managers on whether and how knowledge leaks occur.

The main limitations arise from the specific research context, in fact the study has been conducted in an aerospace cluster. So future studies might consider to explore knowledge leaks in non-cluster settings and in low tech industries.

The results have practical implications both for policy makers and for managers. First of all, the research confirms how open innovation often originates from a combination of different knowledge types acquired through the collaboration with heterogeneous players, start-ups included. Hence, managers may design open innovation strategies balancing their portfolio of collaborations to maximize the absorption of relevant knowledge and start-uppers may consider to engage in open innovation practices to accelerate knowledge absorption. Nevertheless, the study warns managers against the risk of knowledge leaks, especially in cases like start-ups where the eagerness to participate or the prestige associated with participating in open innovation networks with key players may hamper the control over knowledge leaks.

This opens up for possible interventions for policy makers too. First of all, policy makers may consider incorporating the concept of knowledge leaks in their campaign in favour of open innovation. Second, the study may help policy makers in designing programmes for knowledge transfer partnerships amongst the various players of a cluster in a more conscious way, especially warning new to business companies, like start-ups, about possible leaks. Finally, there is also the need of developing professional figures like consultants capable of supporting start-ups in their open innovation practices.

Findings reported in the paper confirm multiplexity and heteromorphism in knowledge exchanges and shed the light on a completely unexplored field (i.e. open innovation and start-ups), focussing on knowledge leaks. Relevant implications for policy makers and managers are included in the study.