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Much forecasting is done by experts, who either make the forecasts themselves or who do opinion research to gather such forecasts. This is consistent with previous knowledge management research that typically has focused on directly soliciting knowledge from those with greater recognized expertise.

However, recent research has found that in some cases, electronic markets, whose participants are not necessarily individual experts, often have been found to be more effective aggregated forecasters. This suggests that knowledge management take a similar tact and expand the perspective to include internal markets. As a result, this chapter extends the use of internal markets to be included in knowledge management, thus expanding the base of knowledge to gathering from nonexperts.

In particular, in this paper I examine the use of human expertise and opinion as a basis to forecast a range of different events. This chapter uses a “knowledge distribution grid” as a basis for understanding which kind of forecasting tool is appropriate for particular forecasting situations. We examine a number of potential sources of forecast information, including knowledge acquisition, Delphi techniques, and internal markets. Each is seen as providing forecasting information for unique settings.

This study aims to investigate the applications of knowledge graphs in developing artificial intelligence (AI) assistants and chatbots by reviewing scholarly publications from different lenses and dimensions. The authors also analyze the various AI approaches used for knowledge graph-driven chatbots and discuss how implementing these techniques makes a difference in technology.

Over recent years, chatbots have emerged as a transformational force in interacting with the digital world in various domains, including customer service and personal assistants. Recently, chatbots have become a revolutionary tool for interacting with the digital world in various contexts, such as personal assistants and customer support. Incorporating knowledge graphs considerably improved the capabilities of chatbots by allowing them access to massive knowledge bases and enhancing their ability to understand queries. Furthermore, knowledge graphs enable chatbots to understand semantic links between elements and improve response quality. This study highlights the role of knowledge graphs in chatbots following a systematic review approach. They have been integrated into major health-care, education and business domains. Beyond improving information retrieval, knowledge graphs enhance the user experience and increase the range of fields in which chatbots can be used. Improving and enriching chatbot answers was also identified as one of the main advantages of knowledge graphs. This enriched response can increase user confidence and improve the accuracy of chatbot interactions, making them more trustworthy information sources.

Knowledge graph-based chatbots leverage extensive data retrieval to provide accurate and enriched responses, increasing user confidence and experience without requiring extensive training. The three major domains where knowledge graph-based chatbots have been used are health care, education and business.

Knowledge graph-based chatbots can better comprehend user queries and respond with relevant information efficiently without extensive training. Furthermore, knowledge graphs enable chatbots to understand semantic links between elements, allowing them to answer complicated and multi-faceted questions. This semantic comprehension improves response quality, making chatbots more successful in providing accurate and valuable information in various domains. Furthermore, knowledge graphs enable chatbots to provide consumers with individualized experiences by storing and recalling individual preferences, history or previous encounters. This study analyzes the role of knowledge graphs in chatbots following a systematic review approach. This study reviewed state-of-the-art articles to understand where and how chatbots have used knowledge graphs. The authors found health care, business and education as three main areas in which knowledge-graph-based chatbots have been mostly used. Chatbots have been developed in text, voice and visuals using various machine learning models, particularly natural language pocessing, to develop recommender systems to recommend suitable items, content or services based on user preferences and item associations.

This paper provides a comprehensive review of the current state of the field in using knowledge graphs in chatbots, focusing on machine learning models, domains and communication channels. The study highlights the prevalence of text and voice channels over visual ones and identifies research gaps and future directions. The paper’s insights can inform the design and development of chatbots using knowledge graphs and benefit both researchers and practitioners in AI, natural language processing and human–computer interaction. The paper is of interest to professionals in domains such as health care, education and business.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

The paper aims to fill this gap by positing a framework that considers the service automation decision as a matter of knowledge management: a choice between human resident and codified knowledge assets.

The paper is a conceptual paper, grounded in the knowledge-based view.

The paper uses the information processing theory, which argues that the level of uncertainty in a process should dictate the type of knowledge deployed, as the contingency for the automation choice, and customer interaction uncertainty as the driver of that contingency. From these ideas, propositions are generated relating customer interaction uncertainty and service automation. Further implications for artificial intelligence (AI) are also explored.

The framework illuminates and informs the strategic choices regarding service automation, including the use of AI in professional services, a timely and highly important topic. It offers a valuable model for practitioners and contributes to the academic literature by pointing the way for future directions for scholarly research.

The purpose of this paper is to explore the effect of “usefulness” and “incentives” on the joint decision to share and use knowledge objects.

Using an experimental design, the authors explore the effects of different incentive systems, the effect of the usefulness of a knowledge management system on intent to contribute and intent to use knowledge, the effect of personal characteristics, specifically an individual's tolerance of ambiguity, and joint endogeneity of contribution and use along with potential complementarity of usefulness and incentives.

For ambiguity tolerant individuals, an incentive mechanism that rewards the contributor for shared knowledge used by a knowledge user, and the knowledge user for the act of reuse, is more effective than a simpler incentive scheme that merely rewards knowledge sharing when usefulness level is low. Ambiguity intolerant individuals react equally to both types of incentive schemes regardless of usefulness. Ambiguity tolerant individuals display weakly complementary levels of sharing in response to coordinated increases in incentives and usefulness levels. This has powerful implications for practice as both incentives and usefulness need not be increased in a concerted manner in order to promote use.

This is an experimental study with the use of student subjects and the usual caveats apply.

The contributions of this paper are deriving practitioner implications for the sharing/use of knowledge, the explicit consideration of ambiguity tolerance, and inclusion of both knowledge contribution and use in one comprehensive model.

The purpose of this paper is to gain a clear understanding of the impact of uncommon use of knowledge (adaptation and augmentation) on the performance of information systems (IS) departments, and to explore the effects of human-resources management (HRM) practices on uncommon use of knowledge.

A questionnaire-based survey was used to measure the constructs of the research model. A survey package was delivered to project managers or team leads and 133 responses were returned.

The empirical results indicate that knowledge adaptation has a significant effect on departmental performance, whereas knowledge augmentation is more important to innovation than to routine departmental performance. The results also show that, while knowledge adaptation can be enhanced by communication and an uncertainty-avoidance culture, knowledge augmentation is an outcome of shared decision-making, the use of teams, and innovation-based policies.

Given the positive impact of uncommon use of knowledge on IS department performance, future research should explore other factors besides HRM practices to boost it.

The results can serve as guidance for managers looking to select HRM practices to promote uncommon use of knowledge.

This study introduces knowledge adaptation and knowledge augmentation as the component processes of uncommon use of knowledge to the IS discipline, and empirically validates the antecedents and consequences of uncommon use of knowledge using survey data.

This paper aims to study the relationship between the knowledge obtained in social networks by firms together with the firms’ knowledge absorptive capacity and organizational unlearning as influencers of service-dominant (S-D) orientation, as well as the mediating role of absorption capacity and organizational unlearning in this process.

The sample amounts to 101 responding companies, obtained from the SABI database. The companies belong to the service sector in Spain and have at least 50 employees and 5 years or more of existence in the market. The model was estimated through partial least squares structural equation modeling with smartPLS software 3.2.6.

The results show that the use of social media is very important for firms to acquire knowledge and capabilities that help them to become S-D oriented. In addition, absorptive capacity and unlearning processes are dynamic capabilities necessary to transform the knowledge acquired in social media and to become S-D oriented.

Companies’ managers draw conclusions that can generate great value, while better meeting the needs and desires of the market, the more knowledge is obtained. In addition, the use of the knowledge generated in this process will reduce the risk of sudden changes in the market.

The more knowledge is gained using social networks, the better the companies’ managers will be able to draw conclusions that can generate great value, while at the same time better satisfying the needs and desires of the market, and thus create capacities that allow the company to be service-oriented. In addition, the use of these knowledge platforms strengthens the strategic advantage of companies through the promotion of innovation and by relating dynamic capabilities with the orientation toward sustainable development. As mentioned above, a process will be generated that will reduce the risk of sudden changes in the market.

This is one of the few studies trying to study the antecedents of S-D orientation and the first to study the direct effect of social media use, and the direct and indirect effect of absorptive capacity and organizational unlearning on S-D orientation as dynamic capabilities.

The purpose of this review is to systematically understand the development of enterprise social media (ESM) research, quantitatively analyze the landscape and track the development of ESM literature and reveal new trends and challenges in ESM research.

Based on 321 relevant literature studies (2005–2020) collected from the Web of Science core collection, the visualization tool CiteSpace is used to conduct bibliometric cocitation and cooccurrence analyses to quantify and visualize the landscape and evolution of ESM research.

Through analyzing the author cocitation network, document cocitation network, journal cocitation network and keywords cooccurrence network, this review proposes an integrated research framework, which highlights major purposes, antecedents and consequences of ESM use in organizations and presents future research trends of ESM research.

Different from the existing qualitative review of ESM, this review adopts bibliometric review to quantify and visualize the landscape of ESM research.

Flock leadership theory explores how different collective learning capacities emerge when interacting individuals work through challenges. While research has link other leadership styles with knowledge management systems (KMS), studies examining flock leadership and KMS are largely lacking. This paper aims to investigate the nexus between flock leadership, knowledge content quality, perceived usefulness of KMS, KMS use for sharing and KMS use for retrieval.

Using survey data (n = 442) from health-care establishments in Jordan, the authors apply structural equation modeling (SEM).

The results show the following: flock leadership influences knowledge content quality; knowledge content quality influences perceived usefulness of KMS; perceived usefulness of KMS influences KMS use for sharing and retrieval; and knowledge content quality and perceived usefulness of KMS mediates the link between flock leadership and KMS use for sharing and retrieval.

Existing research considered KMS through a narrow lens, without consideration of the social context (collective capacities) that surrounds knowledge workers. This study responds to call for research concerning the motion that says technical and adaptive capacity are the underlying norm that configures flock behaviors. Implications for research and practice are discussed.

The purpose of this paper is to present a study of the understanding and usage of social networking sites (SNS) as a knowledge management (KM) tool in knowledge‐intensive enterprises.

In terms of research approach, the study has taken an interpretitivist framework, using a higher education (HE) institution as the case‐study, which is characterised by the need to generate process, share and use knowledge on a daily basis in order to remain competitive. The case study was analysed using qualitative research methodology, composed of interviews and utilised narrative analysis as a means of data analysis, thus deriving a characterisation of understandings, perceptions and acceptance of SNS as a KM tool.

The study provides evidence that even in HE, where it is generally acknowledged that there is a need to adequately capture, store, share and disseminate knowledge, as this can lead to greater innovation, creativity and productivity, participants were suspicious of the nature of the technology and the fact that it could intertwine their professional and social life. As a result, they were not prepared to invest the relatively high effort required in employing SNS as a KM tool as they also have difficulty in establishing the added value. Consequently, in order to employ SNS for KM purposes cultural, behavioural and organisational issues need to be tackled before even considering technical issues.

The paper provides an insight into KM and social networking in HE. This also highlights issue for international HE.