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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.

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 authors examine the spillover effects of CEO removal on the corporate financial policies of competing firms among S&P 1500 firms.

The authors used generalized estimating equations (GEE) on a sample of S&P 1,500 firms from 2000 to 2018 to test this study's research hypotheses. Return on assets (ROA), investment policy, and payout policy are used as proxies for corporate policies.

The authors found an increase in ROA and dividend payout in the immediate aftermath. Further, this study's hypothesis does not hold for R&D expenditure and net-working capital as the authors found an insignificant change in them in the immediate aftermath. However, the authors found a significant reduction in capital expenditure, supporting this study's hypothesis in the context of investment policy. Institutional investors and product similarity moderated the spillover effect on corporate policies (ROA, dividend payout, and capital expenditure).

The authors address a novel aspect of CEO performance-induced removal due to poor performance, i.e., the response of other CEOs to CEO performance-induced removal. This study's findings add to the literature supporting the bright side of CEOs' response to CEO performance-induced removal in peer firms due to poor performance.

This study focused on nonconventional marketing (in-game advertisement interactivity) to understand consumers' purchase intentions. Specifically, this study aims to explore the intervening role of feelings of presence between in-game advertisement interactivity and consumer purchase intention. It further explores whether game–product congruence moderates the relationship between feelings of presence in the game and consumer purchase intention.

This study collected data from 386 PUBG mobile players on a convenience basis. The selected players were ensured to have experience in playing mission ignition mode (a Tesla-themed mode of PUBG mobile).

The study noted that in-game advertisement interactivity positively affects consumer purchase intention, and this relationship is further explained through feelings of presence. In addition, game–product congruence was noted to strengthen the association between feelings of presence and consumer purchase intention.

The study used a cross-section design to collect data from players of PUBG through Google Forms on a convenient basis. The study highlights the significance of nontraditional advertisement and game–product congruence that helps businesses to attract customers.

Drawing upon transportation theory, this study is the first of its kind that has explored the mediating role of feelings of presence between in-game advertising interactivity and consumer purchase intention. In addition, this study shed light on the importance of game–product congruence to strengthen purchase intention.

The study seeks to evaluate the extent and quality of environmental reporting following a longitudinal analysis and covering a wide spectrum of industries in a single frame. The study also attempts to identify the set of most favored environmental reporting items by firms and items which are least disclosed. Furthermore, the study attempts to test whether certain corporate attributes such as firm size, age of the firm, leverage ratio, profitability, presence of independent directors in the board and gender diversity have any influencing power over environmental disclosure practices. The whole study has been carried out from legitimacy theory setting.

The study follows longitudinal analysis to identify the extent and quality of environmental disclosures. A self-constructed checklist of 12 environmental reporting items has been developed analyzing the annual report and content analysis method is followed to measure the extent and quality of environmental disclosures and identify environmental reporting items which are mostly disclosed and which are least disclosed. The study further uses panel data regression analysis to investigate whether certain corporate attributes have any impact on environmental disclosures using multiple linear regression. Total of 345 annual reports of listed financial and nonfinancial institutions have been observed in this study ranging from 2015 to 2019.

The key finding suggests that strict enforcement of Green Banking Rules 2011 fosters country’s commercial banks to invest more to protect the environment and commercial banks encourage nonfinancial institutions for environmental performance and related disclosures through finance. Therefore, almost 50% of sample firms disclose their environmental performance through reporting in either narrative, quantitative or monetary format which was only 2.23% in the last decade. Findings also reveal that tree plantation is the most reported environment disclosure followed by investment in renewable energy and green infrastructural projects and the least reported items are fund allocation for climatic changes and carbon management policy. Further analysis shows that firm size and leverage ratio both have positive impact on environmental reporting.

An in-depth analysis may be conducted to identify why certain environmental items are least disclosed such as fund allotment for climatic changes, carbon management policy, etc. and how corporations may earn social appreciation and motivation by investing in those least preferred items in legitimacy theory setting. Future research may also take into consideration other corporate attributes which are not considered in the study.

The study conducted an in-depth analysis to understand the most favored form of environmental disclosures (narrative/quantitative/monetary) and their extent after incorporation of regulatory guidelines, which is the first of its kind in the research of environmental disclosures. The study indeed contributes to the documentation of environmental reporting in the context of a developing country where there is a lack of longitudinal analysis from the lens of legitimacy theory. Moreover, a wide spectrum of industries has been taken into consideration which facilitates the generalized findings on the environmental disclosure practices of corporations in Bangladesh.

本研究擬評估公司報告環境方面的程度和質量, 以及對就環境報告披露而言、最受青睞和最不受歡迎的項目加以處理。研究亦擬測試企業屬性對實踐環境信息披露的影響。

研究使用內容分析法、去測量環境信息披露的程度和質量。研究使用多元回歸分析、去探討企業屬性對環境信息披露的影響。研究涵蓋孟加拉國上市公司共345個年度報告, 涵蓋的年期為2015年至 2019年。

研究結果似乎顯示綠色金融規則 - 2011 、成功鼓勵機構為保護環境而投放更多資源; 機構最樂於匯報的項目為植樹, 而披露最少的則為氣候變化和碳管理政策。進一步的研究分析顯示, 公司的規模和杠杆比率均會對環境匯報帶來正面的影響。

本研究豐富了關於發展中國家環境匯報的官方文件記錄, 而在這類國家, 透過合法化理論而進行的縱貫性分析研究頗為缺乏。本研究以深度分析法、去瞭解環境信息披露方面最受青睞的信息披露方式 (故事形式的敘述/定量形式/金融形式), 也去瞭解納入強制的規管指引後環境信息披露的程度; 就此而言, 本研究為這類環境信息披露研究的首個研究。

This study aims to investigate the key determinants predicting users’ behavioural intention (BI) in adopting mobile payment (m-payment) in the new normal era.

The mobile technology acceptance model (MTAM) was extended through attitudes, perceived trust, perceived risk and personal innovativeness (PI) with government support (GS) functioning as a moderator. A total of 245 valid responses were gathered from Malaysian m-payment users with purposive sampling and subsequently evaluated through partial least square-structural equation modelling.

Mobile usefulness and PI significantly predicted user BI to use m-payment. Based on the moderation analysis, GS strengthened attitude-based impacts on BI towards m-payment adoption.

The empirical outcomes provide stakeholders with pivotal implications to develop holistic policies and strategies that potentially catalyse m-payment usage in the new normal era.

This research expands the current body of knowledge by assessing the factors impacting m-payment usage intention in the new normal era. The four aforementioned MTAM elements and GS (moderator) were recommended to boost model workability and offer novel evidence from a distinct viewpoint.

El objetivo de este estudio es investigar los determinantes clave que predicen la intención de comportamiento de los usuarios a la hora de adoptar el pago por móvil (m-payment) en la nueva era normal.

El modelo de aceptación de la tecnología móvil (MTAM) se amplió a través de las actitudes, la confianza percibida, el riesgo percibido y la capacidad de innovación personal, con el apoyo gubernamental como moderador. Se recogió un total de 245 respuestas válidas de usuarios malasios de pago por móvil mediante muestreo intencionado y se evaluó posteriormente mediante modelización de ecuaciones estructurales por mínimos cuadrados parciales (PLS-SEM).

La utilidad del móvil y la capacidad de innovación personal predijeron significativamente la intención de los usuarios de utilizar el pago por móvil. Según el análisis de moderación, el apoyo gubernamental reforzó los efectos basados en la actitud sobre la intención conductual de adoptar el pago por móvil.

Los resultados empíricos proporcionan a las partes interesadas implicaciones fundamentales para desarrollar políticas y estrategias holísticas que catalicen potencialmente el uso del pago móvil en la nueva era de la normalidad.

Esta investigación amplía el corpus actual de conocimientos al evaluar los factores que influyen en la intención de uso del pago por móvil en la nueva era normal. Se recomiendan los cuatro elementos MTAM mencionados y el apoyo gubernamental (moderador) para impulsar la viabilidad del modelo y ofrecer pruebas novedosas desde un punto de vista distinto.

本研究旨在探讨新常态时代用户使用移动支付（m-payment）行为意向的主要决定因素。

通过态度、感知信任、感知风险和个人创新能力, 并以政府支持作为调节因素, 对移动技术接受模型（MTAM）进行了扩展。通过有目的的抽样, 从马来西亚移动支付用户中收集了 245 份有效回复, 随后通过偏最小二乘法结构方程模型（PLS-SEM）进行了评估。

移动实用性和个人创新性可显著预测用户使用移动支付的行为意向。根据调节分析, 政府支持加强了态度对采用移动支付的行为意向的影响。

实证研究的结果为利益相关者提供了重要的启示, 有助于他们制定全面的政策和战略, 在新常态时代促进移动支付的使用。

本研究通过评估新常态时代影响移动支付使用意向的因素, 拓展了现有的知识体系。研究推荐了上述四个 MTAM 要素和政府支持（调节器）, 以提高模型的可操作性, 并从一个独特的视角提供了新的证据。

This paper aims to create a conceptual model that connects learning organizations, service innovation and technology acceptance.

The importance of the interaction of variables benefiting both individuals and organizations has been comprehensively explained by combining two well-known theories – learning organizational theory and service innovation theory – with the technology acceptance model. In the first part of the study, conceptual model has been constructed and then applied to the hospitality industry of which results have been presented in the second part of this paper.

It is hypothesized that learning organization, service innovation and technology acceptance have significant relationships. It is also suggested that the learning organization plays an intermediary role in the relationship between technology acceptance and service innovation. Empirical results in this regard have been presented in the second part of the paper.

The relations have been established and tested in the hospitality industry in Antalya. However, the model can be applied and established relations tested in other industries.

This research contributes to our knowledge of the intricate linkages that exist between learning organizations, technology acceptance and service innovation. Originality of the paper is related to the novel multilayered model illustrating three-way interactions between the three dimensions of learning organization, technology acceptance and service innovation.

Healthcare workers are considered to be the most vulnerable to face mental health. Therefore, this paper aims to examine how negative leadership (despotic leadership) affects employees' psychological distress. Specifically, the authors investigated bullying behavior as mediating mechanism and hostile attribution bias as boundary condition that trigger psychological distress.

The authors collected data from 252 nurses and their immediate supervisors (as a coping strategy for common method bias) through “Google Forms” from various public and private hospitals.

The authors applied structural equation modeling and noted that despotic leadership positively affects employees' psychological distress through bullying behavior. In addition, hostile attribution bias is identified as an important factor in amplifying the effect of bullying behavior on psychological distress.

The authors collected data from high-power distance culture where negative leadership is more prevalent as compared to low-power distance culture. Their findings suggest management to discourage self-centered leaders (despotic) and employees with negative personality traits (hostile attribution bias) as these affect their mental health.

Drawing upon conservation of resources theory, this study is the first of its kind that has investigated how and when despotic leadership affects employees' psychological distress. In addition, the authors also highlighted the importance of negative personality traits (hostile attribution bias) that can amplify the association between bullying behavior and psychological distress.