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In recent years, data science has become a high-demand profession, thereby attracting transmuters (individuals who want to change their profession due to industry trends) to this field. The primary purpose of this paper is to guide transmuters in becoming data scientists.

An exploratory study was conducted to uncover the challenges faced by data scientists according to their educational backgrounds. An extensive set of responses from 31 countries was received.

The results reveal that skill requirements and tool usage vary significantly with educational background. However, regardless of differences in academic background, the data scientists surveyed spend more time analyzing data than operationalizing insight.

The collected data are available to support replication in various scenarios, for example, for use as a roadmap for those with an educational background in art-related disciplines. Additional empirical studies can also be conducted specific to geographical location.

The current work has categorized data scientists by their fields of study making it easier for universities and online academies to suggest required knowledge (courses) according to prospective students' educational background.

The conducted study suggests the required knowledge and skills for transmuters to acquire, based on their educational background, and reports a set of motivational factors attracting them to adopt the data science field.

Knowledge sharing has become necessary for organizations as it is a source of competitive advantage. Therefore, this study aims to investigate how empowering leadership through psychological empowerment encourages employees’ knowledge-sharing (KS) behavior. This study further explores the moderating role of learning goal orientation (LGO) between psychological empowerment and KS behavior.

This study collected data from 423 employees working in manufacturing and service organizations in two waves on convenience basis to tackle common method variance. The data were analyzed through structural equation modeling.

The results revealed that empowering leadership helps employees modify their KS behavior both directly and indirectly in the presence of psychological empowerment. Moreover, individuals high in LGO are more likely to involve in KS behavior when psychologically empowered.

This study used a cross-sectional design and suggests management focus on their working environment to enhance knowledge sharing among employees, which is possible through empowering leaders. The study further suggests management not ignore individual attributes during recruitment.

Drawing upon job characteristics model and social exchange theory, this study explores the mediating role of psychological empowerment between empowering leadership and KS behavior and the moderating role of LGO on the association between psychological empowerment and KS behavior.

Enhancing green innovation for corporate sustainability is one of the recent issues globally. Knowledge management has been determined as a core factor that hamstrings green innovation. The existing literature was limited to expose the importance of the knowledge management process for corporate sustainable performance. Thus, this paper aims to examine the role of the knowledge management process for corporate sustainable performance with the integration of green innovation and organizational agility following the resource-based view theory.

Cross-sectional design was used in this study. Data were gathered through convenience sampling from 475 respondents of multinational manufacturing corporations of Pakistan, analyzed by using structural equation modeling.

This study revealed that the knowledge management process and its all constructs (acquisition, dissemination and application) lead toward green innovation; further, green innovation influences corporate sustainable performance and its all constructs (environment, economic and social). Green innovation partially mediates the association between the knowledge management process and corporate sustainable performance. Besides, organizational agility has a positive effect on green innovation and corporate sustainable performance but was not found moderating these relations. The study educates that organizations investing in innovative technologies and adopting greener strategies are not only adequate for achieving sustainable performance, soft issues such as knowledge management and organizational agility but also important factors in the current knowledge base economy.

This study is an attempt to examine the previously undiscovered multi-dimensional relationships among the knowledge management process, green innovation, organizational agility and corporate sustainable performance. The presence of a positive correlation among these constructs was observed, proving the conceptual framework for this study.

Innovation has become a necessity for the information technology (IT) sector, especially during COVID-19 pandemic. Therefore, this study aims to investigate how knowledge sharing affects employees’ innovative work behavior (IWB). Specifically, the study examined occupational self-efficacy (as mediating mechanism) and entrepreneurial leadership (as boundary condition) to encourage IWB.

The study used social media platforms to collect data from 270 employees working in the IT sector through “google forms” on convenience basis between March and August, 2021. The study applied structural equation modeling in two stages to examine the measurement model (for uni-dimensionality) and the structural model (for hypotheses testing).

The study noted that knowledge sharing positively affects employees’ IWB and occupational self-efficacy positively explains this association. In addition, employees’ perception of entrepreneurial leadership strengthens the association between knowledge sharing and IWB.

The study collected data from a developing country during COVID-19 by using a cross-sectional design that may restrict causality. However, the findings suggest the management not only encourages knowledge sharing environment but also engages employees in various training that motivate them to experiment with new ideas and techniques.

This study extends the existing literature on knowledge sharing and IWB by exploring occupational self-efficacy as mediating mechanism and entrepreneurial leadership as a boundary condition.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

The aim of this paper is to develop a model for presenting level of trust and analyzing the contribution of various trust-building measures undertaken by an organization.

The conceptual framework for the model is based on previous research and the concept of trust and its implications in business environment. This model includes various stages of trust measured against time and trust building measures (TBM). This trust model relates the trustee’s position with the trustor at any point in time and describes its impact on trustee’s position in terms of trustworthiness (sum of “trust deficit” and “trust gain”). 10;Vectors and linear Algebra equations are used to construct the model supplemented with an example from real-life business environment for better understanding of the model. 10.

A trust framework, elaborating level of trust between two parties is explained with the help of a mathematical model. The model includes various stages of trust measured against time and TBM.

In the practical application of the model, the authors adopted an existing scale to measure trust levels, which can have its limitations and shortcomings. It is however suggested to choose as specific scale for the industry as possible.

The model can be applied in any situation, person or environment specially to determine the current situation of organizational trust in business which can be helpful in making decisions.

The concept of making trust a part of strategy and a tool for decision-making is novel and applicable in all sectors and situations. By providing a real-time view of the level of trust and impact of TBM will help predict future levels of trust and make it an essential part of decision-making process.

The purpose of this paper is to examine the impact and interrelation between positive psychological capital and occupational burnout among faculty associates of technical and professional training institutions.

In total, 282 faculty members from 17 technical institutions were selected from the province of Punjab, Pakistan. Results were drawn using bivariate correlation and linear regression on the data that were collected through adopted questionnaire.

Results of the study revealed that occupational burnout is significantly related to the performance of the faculty of technical institutes and that psychological capital moderates this relationship.

Conclusions from this research suggested that the personality trait of a psychological capital is an essential determinant of job burnout and performance among faculty members; therefore, it must be considered as an essential part of the selection criteria and job-assessment process. This is important, since psychological capital reduces the detrimental impact of occupational burnout on performance outcomes. Further the faculty members with higher psychological capital should be retained to create positive work environment which can be an inspiration for others.

The research provides important information about the impact of personality traits upon the performance of a faculty member who joins an educational institution coming from an industrial environment. Although having similar technological skills required for the position, still the effects of a new environment can cause performance issues, the research insisted that positive personality traits like psychological capital can help in gaining required confidence to perform in a changed environment.

This study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations.

In this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004–2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies.

The outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers’ challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers; (2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques); (3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context; (4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques; (5) performing a SWOT analysis on the selected studies; (6) based on the developer’s challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context.

This study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can overcome the identified challenges (including mashup-oriented API, Time-consuming process, learn how to use the API, integrated problem, API method usage location and limited usage of code) in the future by proposing a framework or recommendation system. Furthermore, the classification of current state-of-the-art API recommendation techniques also helps the researchers who wish to work in the future in the context of API recommendation.

This study not only facilitates the researcher but also facilitates the practitioners in several ways. The current study guides the developer in minimizing the development time in terms of selecting relevant APIs rather than following traditional manual selection. Moreover, this study facilitates integrating APIs in a project. Thus, the recommendation system saves the time for developers, and increases their productivity.

API recommendation remains an active area of research in web and mobile-based applications development. The authors believe that this study acts as a useful tool for the interested researchers and practitioners as it will contribute to the body of knowledge in API recommendations context.

Similarly, Zhu et al. (2014) and Zhang et al. (2014) stated that addressing privacy concerns with the recommendation process is necessary for the healthy development of app recommendation. Recently, Xiao et al. (2020) mentioned that a lack of effective privacy policy hinders the development of personalized recommendation services. According to the reported work, privacy protection technology methods are too limited for mobile focusing on data encryption, anonymity, disturbance, elimination of redundant data to protect the recommendation process from privacy breaches. So, this situation motivated us to conduct a systematic literature review (SLR) to provide the viewpoint of privacy and security concerns as mentioned in current state-of-the-art in the mobile app recommendation domain.

In this work, the authors have followed Kitchenham guidelines (Kitchenham and Charters, 2007) to devise the SLR process. According to the guidelines, the SLR process has three main phases: (1) define, (2) conduct the search and (3) report the results. Furthermore, the authors used systematic mapping approach as well to ensure the whole process.

Based on the selected studies, the authors proposed three main thematic taxonomies, including architectural style, security and privacy strategies, and user-usage in the mobile app recommendation domain. From the studies' synthesis viewpoint, it is observed that the majority of the research efforts have focused on the movie recommendation field, while the mainly used privacy scheme is homomorphic encryption. Finally, the authors suggested a set of future research dimensions useful for the potential researchers interested to perform the research in the mobile app recommendation domain.

This is an SLR article, based on existing published research, where the authors identified key issues and future directions.

Developing on the base of theory of planned behaviour (TPB), the purpose of this paper is to investigate the relationship between perceived social support (SS) from one’s social network and entrepreneurial intention (EI). Moreover, mediating effect of other constructs of TPB, i.e., attitude towards entrepreneurship (ATE), subjective norms (SN) and perceived behavioural control (PBC) is also examined in this study.

This study involves a variance-based partial least square-structural equation modelling approach for analysing responses from 381 fresh business graduates.

Findings of this study revealed that SS positively influences EI and also that this relationship is fully mediated by ATE, SN and PBC.

Due to limited resources, cross cultural comparison and multi-group analysis were not performed, which are considered as a limitation of this study.

It is expected that the findings of this study can help policy makers, researchers and academicians in better understanding of critical role of SS for understanding the intentions of nascent entrepreneurs.

Further, findings of this study suggest that academicians and policy makers need to take heed towards relatively less explored phenomenon of SS to enhance the attractiveness of entrepreneurial career in fresh business graduates.

This study has proposed a model for assessing impact of SS on EI. By doing so, this study extends TPB in the context of EI. Moreover, findings of this study are a unique step forward, and offer a new insight towards better understanding of the determinants of EI in fresh business graduates.