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The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual knowledge workers across varied disciplines.

The methodology involves four steps, i.e. literature search, screening and selection of relevant data, data analysis and data synthesis related to KM, PKM and generative artificial intelligence (AI) with a focus on ChatGPT. The findings are then synthesized to develop a viewpoint on the challenges and opportunities brought by ChatGPT for individual knowledge workers in enhancing their PKM capability.

This work highlights the prevailing challenges and opportunities experienced by knowledge workers while leveraging PKM through implying ChatGPT. It also encapsulates how some management theories back the cruciality of generative AI (specifically ChatGPT) for PKM.

This study identifies the challenges and opportunities. from existing studies and does not imply empirical data/result. The authors believe that findings can be adjusted to diverse domains regarding knowledge workers’ PKM endeavors. This paper draws some conclusions and calls for further empirical research.

ChatGPT’s capability to accelerate organizational performance compelled scholars to focus in this domain. The linkage of ChatGPT to Knowledge Management is an under-explored area specifically the role of ChatGPT on PKM hasn't been given attention in the existing work. This is one of the earliest studies to explore this context.

This theoretical paper introduces a conceptual framework for Personal Cultural Heritage Management (PCHM), derived from prior research on migrants' information practices. It elaborates on the literature background and the development of the PCHM framework, highlighting the role of personal information management (PIM) and personal collections in the creation, access and utilization of cultural heritage information.

The study describes and explains the construction of the PCHM framework as a structured and self-motivated approach to personal heritage and identity learning.

Following the theoretical background and assumptions, along with the presentation of the key building blocks, the paper describes the key components of the framework, outlines their definitions and provides examples.

Theoretically, PCHM extends the current literature by encapsulating processes and actions employed by individuals to manage personal collections for cultural identity purposes, thereby underscoring the critical role personal collections play in both preserving and communicating cultural heritage.

PCHM can guide the development of support systems and policies to enhance cultural continuity and integration, thus empowering individuals to navigate their cultural identities confidently.

The PCHM framework creates a unique intersection between PIM and cultural heritage, providing a new perspective for understanding the dynamic evolution and formation of cultural identity among migrants.

Generative AI and more specifically ChatGPT has brought a revolution in the lives  of people by providing them with required knowledge that it has learnt from an exponentially large knowledge base. In this viewpoint, we are initiating the debate and offer the first step towards Generative AI based knowledge management systems in organizations.

This study is a viewpoint and develops a conceptual foundation using existing literature on how ChatGPT can enhance the KM capability based on Nonaka’s SECI model. It further supports the concept by collecting data from a public sector univesity in Hong Kong to strenghten our argument of ChatGPT mediated knowledge management system.

We posit that all four processes, that is Socialization, Externalization, Combination and Internalization can significantly improve when integrated with ChatGPT. ChatGPT users are, in general, satisfied with the use of ChatGPT being capable of facilitating knowledge generation and flow in organizations.

The study provides a conceptual foundation to further the knowledge on how ChatGPT can be integrated within organizations to enhance the knowledge management capability of organizations. Further, it develops an understanding on how managers and executives can use ChatGPT for effective knowledge management through improving the four processes of Nonaka’s SECI model.

This is one of the earliest studies on the linkage of knowledge management with ChatGPT and lays a foundation for ChatGPT mediated knowledge management system in organizations.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Construction organizations' investment in effective talent-development programs is a key strategy in attracting, developing and retaining staff. Such programs are especially important given the current challenges in the construction workforce, including labor shortages, an aging workforce, generational differences in the workforce, supply chain disruptions and the need to effectively train staff in the skills that are essential in a constrained labor environment. To address these challenges, this study proposes a performance measurement strategy that construction companies can use as input to design their talent development programs.

The strategy intends to assess the performance of project managers and develop criteria that define categories of their performance, including the top performers' category. This enables construction organizations to provide each project manager with individualized training that addresses areas of weakness and in turn, develops the skills that correspond with being top performers. The proposed strategy was developed and tested by surveying the immediate supervisors of 187 project managers working for general and specialty contractors in the United States. Principal component analysis was used to develop a single performance construct from seven performance criteria.

This construct was used to organize the project managers into the categories of top, above-average and below-average performers. According to the findings, top-performing project managers have well-rounded skills in the areas of leadership, communication, technical proficiency and overall job knowledge.

The outcomes of this study can help construction organizations focus their talent-development programs on the skills most associated with PMs being top performers.

This study provides construction organizations with a comprehensive performance-measuring construct to focus their talent-development programs on the skills most associated with top-performing project managers. Researchers can use this study as a foundation for further understanding how performance is related to various construction professions.

Various studies have been conducted to explore the factors that are important to be considered for the effectiveness of construction contract administration (CCA) but lack in the context of graduate architects. The purpose of this study is to identify factors influencing the effectiveness of graduate architects in CCA and possible methods to enhance their work efficiency through developing the relevant skills in a changing construction environment.

This paper identified the factors that influence the effectiveness of graduate architects in CCA through the quantitative methods. General skill elements that are perceived as essential for the effectiveness of CCA had been investigated through a survey of graduate architects in the construction industry. One hundred and twenty-seven completed questionnaires returned were analyzed and tested using descriptive analysis and relative important index (RII).

The result from the study showed that the factors influencing the effectiveness of graduate architects as CCA are building construction skills, design management skills, project management skills, soft skills and dispute resolution skills.

The contribution of this study can be utilized for developing models/tools in the future that would improve the work performance of graduate architects in CCA. Educators may utilize this study to improve their syllabus to cater to the market's demand and facilitate students' entry into the labor market.

Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics.

A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature.

The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media.

Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.

The Great Resignation has led to a significant increase in the number of people quitting their jobs due to reasons such as stagnant wages, rising cost of living, job dissatisfaction and safety concerns. Therefore, the aim of this study is that it is important to help people develop better cognitive resources to face adversity.

The Great Resignation has led to a significant increase in the number of people quitting their jobs due to reasons such as stagnant wages, rising cost of living, job dissatisfaction and safety concerns. Therefore, it is important to help people develop better cognitive resources to face adversity. In this study, we administered a questionnaire to 250 employees to determine the variables that could help them build cognitive resources. These variables included the satisfaction of basic psychological needs (autonomy, competence and affiliation), psychological capital, motivation regulation (within the self-determination theory) and well-being (assessed by self-esteem, positive emotions, positive automatic thoughts and vitality). The results revealed that satisfaction of basic needs is associated with better psychological capital and more self-autonomous behavior, which leads to higher psychological well-being. These findings are discussed in the paper, emphasizing the importance of management and work context that satisfy the basic needs and help to build resources with psychological capital.

The results revealed that satisfaction of basic needs is associated with better psychological capital and more self-autonomous behavior, which leads to higher psychological well-being. These findings are discussed in the paper, emphasizing the importance of management and work context that satisfy the basic needs and help to build resources with psychological capital.

Highlight the importance of consequences of the Great Resignation and the need to internationalize this concept.

The purpose of the study is to identify how knowledge management processes impact innovation performance in the Jordanian medical sector (private hospitals) as well as identify how big data analytics moderates this performance.

Two hundred ninety-one questionnaires were analyzed for the purpose of this study. A structural equation model (SEM) was used to test convergence validity, discriminant validity and reliability. In order to analyze the data, bootstrapping was used.

The empirical results showed that all knowledge management processes are statistically significant in influencing innovation performance. Furthermore, big data analytics moderates the relationship between knowledge management processes and innovation performance.

The results of this cross-sectional study are limited to one country and one industry due to methodological limitations, and the results represent a snapshot at a particular point in time.

Jordan's medical leaders will benefit from this study, since it emphasizes the importance of knowledge management processes to enhance innovation performance, especially given the importance of big data analytics in the field, increasing innovation capabilities in the medical field, thereby increasing innovation levels.

Taking job demand-resource (JD-R) and self-determination perspective, the current study focused to see how basic need satisfaction (BNS) – as a personal demand – impacts work engagement directly and indirectly through personal resource (i.e. self-efficacy). Moreover, the aim was to test the dimension-wise impact of BNS, i.e. the need for autonomy, need for belongingness and need for competence in the aforementioned relationship.

This research is a time-lagged survey in which three-wave data of 398 white-collar employees were collected from the service and manufacturing sector of Pakistan through convenience sampling. Each wave of data collection was two months apart. The matched responses yielded an overall response rate of 66.33%. The collected responses were duly analysed using partial least squares structural equation modeling (PLS-SEM).

Results of the study confirmed all direct and indirect hypotheses encompassing the impact of the combined BNS construct on work engagement via self-efficacy. Nonetheless, in the dimension-wise analysis, the indirect impact of the need for job autonomy on work engagement was not validated. This depicted that the need for competence and relatedness are more important predictors of work engagement through the self-efficacy path.

It has been observed that prior research on work engagement was mainly focused on the role of job demands (JDs) and personal resources; however, the role of personal demands along with personal resources has little been discussed. The authors tested the total as well as the specific impact of each component of basic need on work engagement making it possible to examine the total predicting role of basic need satisfaction and the specific contribution of satisfaction of each need on work engagement.