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Purpose: The study aims to analyse and discuss the effect of COVID-19 on businesses. The chapter discusses the various machine learning (ML) tools and techniques, which can help in better decision making by businesses in the present world.

Need for the Study: COVID-19 has increased the role of VUCA elements in the business environment, and there is a need to address the challenges faced by businesses in such environment. ML and artificial learning can help businesses in facing such challenges.

Methodology: The focus and approach of the chapter are in the context of using artificial intelligence (AI) and ML techniques for decision making during the COVID-19 pandemic in a VUCA business environment.

Findings: The key findings and their implications emphasise the importance of understanding and implementing AI and ML techniques in business strategies during times of crisis.

Practical Implications: The chapter’s content is in the context of using AI and ML techniques during the COVID-19 pandemic and in a VUCA business environment.

With the increasing use of social media in operation and supply chain management (OSCM), it is of great importance for managers to consider risks in advance and take precautions against the risks that might arise from social media usage among supply chain members. The aim of this research is to identify and evaluate the risks related to the use of social media in OSCM.

An initial research of the literature revealed that there is no detailed risk categorization in this area. Current taxonomies on the business risks of social media usage were examined and integrated with classifications identified in a Delphi study. The authors empirically demonstrate how the determined risks are prioritized and how decision-makers may decide to manage risks effectively based on the analytical hierarchy process (AHP) method.

The findings of the research showed that reputation-associated risks such as criticism, language and loss of confidence should be prioritized over human, content and technical-associated risks.

To date, a comprehensive approach to determine risks arising from using social media in OSCM is missing. With the Delphi and AHP techniques, the authors provide a novel insight for managers to mitigate risks. The outcomes of this study may assist executives in achieving successful management of social media usage in OSCM. Besides, the proposed AHP model may provide guidelines and direction in this regard.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.

This study analyses configurations of national culture as boundary conditions of countries’ national systems of innovation (NSI). Drawing from the NSI approach, we argue that culture’s role is that of a contingency factor shaping the relationship between investments in innovation and national innovation outputs.

We assessed the moderation effect of national culture through a systematic, two-stage approach using fuzzy-set Qualitative Comparative Analysis (fsQCA), which allows the analysis of changes induced by the moderator variables. Analyses were conducted with a diverse sample of 61 countries over a period spanning 12 years, from 2011 to 2022.

Findings reveal that investments in innovation, but not individual cultural dimensions, is a necessary condition for high innovation outputs. Furthermore, several configurations of cultural dimensions were identified as moderators of the relationship between investments in innovation and innovation outputs.

This study provides insights into cross-national innovation research by exposing the role of cultural configurations, rather than just individual cultural dimensions, as boundary conditions involved in the achievement of high levels of innovation.

The objective of this study was to conduct a bibliometric analysis of the existing literature on organizational deviance to assess how far this concept has progressed since its introduction in the domain of organizational behavior.

This study employs bibliometric methodologies (citation analysis, co-citation analysis and co-occurrence of author keywords) using VOSviewer. The Scopus database was used, as it is the largest database of scholarly literature.

The findings indicate the character and direction of organizational research over the past two decades. Organizational deviance due to psychological contract breach, organizational deviance in the context of organizational cynicism and organizational deviance in the context of psychological capital are the three major themes in the literature on organizational deviance. In addition, the study highlights the most significant authors, journals, institutions and nations in the field of value co-creation research as well as potential future research areas in this area.

The use of a single database and the inability to contextualize the citation structure of papers revealed by the review are limitations of this study.

This study examines the structure of the literature on organizational deviance and charts the field's evolution over time.

This study aims to investigate the factors affecting technological innovation intentions at banks listed in Vietnam, a developing country, to develop business activities and accounting benefits according to the trend of the 4th Industrial Revolution.

To collect and analyze the data for this study, qualitative and quantitative methods were used. Specifically, 20 finance and banking experts and 45 managers in the field of information technology were interviewed in qualitative research over a period of three months. Then, 1,000 questionnaires were sent to banks within six months, with the final sample for quantitative research being 324 respondents. Finally, the structural equation modeling (SEM) was used to check the hypotheses. Regarding the tools used, the qualitative study used a semistructured questionnaire to collect information. Meanwhile, SPSS software was used to analyze quantitative research information, including checking common method bias, nonresponse bias, evaluating scale quality and checking SEM.

The findings show that the usefulness, ease of application, credibility, innovation and efficiency of technology have certain impacts on technological innovation intentions at banks listed in Vietnam. Using the SEM analysis, the results showed that the five factors had a favorable influence on the technological innovation intentions. More specifically, this study proposed adding an efficiency factor, and the results showed that it has the greatest impact on technological innovation intentions.

This study would be considered a continuation of prior studies because it provides empirical evidence for business models at banks listed in developing countries (for example, Vietnam) and so provides useful advice for bank management not only in Vietnam but across Asia. In fact, bank managers should consider introducing new technology as appropriate to make their reports more clear and up-to-date, therefore improving their performance. Banking managers, in particular, should focus on enhancing the bank’s application technology indicators to obtain a competitive edge.

This is a pioneering study that uses a combination of the reasoned action theory, planned behavior theory, transaction cost theory and unified theory of acceptance and use of technology to expand knowledge about technological innovation intentions at listed banks in the context of a developing country. The study also discovered and added the efficiency factor as a key factor affecting the intention to innovate technology at listed banks. These contribute to improving the literature of technological innovation intentions.

This study explores match related factors and their impact on the batting strike rate in Twenty20 cricket – an aspect which can generate excitement and fan engagement in cricket matches.

Data was collected from www.cricinfo.com using a web scraping tool based on R programming from February 17, 2005, to October 25, 2022, numbering 4,221 men’s Twenty20 international innings featuring 41 national teams that had taken place in 85 venues across 11 countries of play. Hypothesis testing was conducted using one-way ANOVA.

The findings indicate that batters score faster in the first inning of a match, and mean strike rates also vary significantly based on the country of play. Further, the study analyses the top performing national sides, venues and country of play in terms of mean batting strike rate, thus providing insights to cricket boards, international regulating bodies of cricket, sponsors, media companies and coaching staff for better decision-making based on batting strike rate.

The originality of the study lies in its focus on using non-marketing strategies to increase fan engagement. Further, this study is the first one to examine different venues from the perspective of batting strike rate in men’s Twenty20 international matches.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

The Purpose of the study was to investigate the construct validity and internal consistency of the LaMI among staff in the context of elderly care in Sweden.

Questionnaire data from a longitudinal study of staff working in elderly care were used. Data were collected using the Leadership and Management Inventory. First data collection was for explorative factor analysis (n = 1,149), and the second collection, one year later, was for confirmatory factor analysis (n = 1,061).

The explorative factor analysis resulted in a two-factor solution that explained 70.2% of the total variance. Different models were tested in the confirmatory factor analysis. The final model, a two-factor solution where three items were omitted, showed acceptable results.

The instrument measures both leadership and management performance and can be used to continually measure managers’ performances as perceived by staff to identify areas for development.

A significant number of criminal and deviant acts are investigated by nonpolice actors. These include private investigators who charge fees for their services, professional services firms such as firms of accountants who also charge fees, in-house investigators employed by private organisations and in-house investigators of public sector organisations who are not sworn police officers. Some of these investigators, such as private investigators, have been exposed in unethical activities such as illegal surveillance and blagging to name some. In this respect, this study aims to uncover the ethical orientations of investigators using cluster analysis.

This study is based upon an online survey of private investigators predominantly in the UK, i.e. investigators beyond the public police. An innovate statistical inferential analysis was used to investigate the sample which resulted in the development of three ethical orientations of such investigators.

Based upon a survey response from 331 of these types of investigators this study illustrates the extent they engage in unethical activities, showing a very small minority of largely private investigators who engage in such activities.

A unique feature of this study is the use of an innovative statistical approach using an unsupervised machine learning model, namely, TwoStep cluster analysis, to successfully group and classify respondents based on their ethical orientation. The model derived three types of ethical orientation: ethical, inbetweeners and risk takers.