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This paper aims to apply a mixed-methods approach to redefine essential work skill sets, propose components of these skill sets that are necessary for workers in the contexts of emerging market economies (EMEs), examine potential selection tools that should be applied across firms and explain implications for the labour process theory (LPT) of work.

During the first phase of this study, qualitative data was collected from methods, including semi-structured interviews with human resources (HR) experts from the Personnel Management Association of Thailand, top and HR executives and managers of firms across industries in Thailand. Non-participant observations were made during the pre-interview, interview and post-interview stages. Analysis of archival documents and Web-based resources was then conducted. The authors used the qualitative data obtained from the first phase to develop survey instruments for conducting quantitative research during the second phase of this study.

The empirical findings demonstrate that essential work skill sets are “soft skills” that help workers survive and thrive in the business arena in EMEs. These essential work skill sets have implications for the LPT of work in that they play an important role in transferring the power of negotiation from employers to workers in the labour market. Essential work skill sets here can be divided into eight skill domains: (1) ideation and system thinking, (2) information and digital literacy, (3) social skills with appreciation for diversity and inclusion, (4) communication and language, (5) creativity and innovation, (6) emotional quotient (EQ) for self-management and development, (7) growth/outward mindset and (8) cognitive skills for the job role. Within each essential work skill set, there are several skills that workers in the current world of work need to possess (42 skills in total). Additionally, potential selection tools include behavioural observation, behavioural-based interviews, STAR (Situation, Task, Action and Results) interviews, role plays, case studies and simulations, high-pressure interview questions, project assignments, assessment centres, in-depth interview questions and special methods, such as face reading and fingerprint reading. Top and HR managers across industries strongly agree that the eight essential work skill sets and 42 skills are necessary for workers to survive in the business arena in EMEs. They also strongly agree that talent selection tools, especially behavioural-based interviews, are used by their firms to select high-skilled job candidates in the labour market.

Because the eight essential skill sets proposed in this paper are based primarily on the qualitative data obtained from top/HR managers in firms across some industries, generalization to respondents across other industries or across other EMEs may be limited. It is possible that the context of other EMEs may be different from that of Thailand. In this regard, some of the essential work skill sets that are suitable in the case of Thailand may not be suitable in the case of other EMEs. Future studies should thus explore how institutional contexts of other countries/economies shape the definition of essential skill sets and their components, as well as potential selection tools that shall be applied to select high-skilled labour in those contexts.

This paper provides practical implications for top managers and/or HR managers of firms across various industries in EMEs. In particular, managers should internally train and develop their employees/workers to possess the eight essential skill sets: (1) ideation and system thinking, (2) information and digital literacy, (3) social skills with appreciation for diversity and inclusion, (4) communication and language, (5) creativity and innovation, (6) EQ for self-management and development, (7) growth/outward mindset and (8) cognitive skills for the specific job role so that their employees/workers can survive and thrive in the era of the brittleness, anxiety, non-linearity and incomprehensibility of the business world under pandemic conditions. Additionally, top managers and/or HR managers of these firms should apply the potential selection tools proposed in this paper to probe into job candidates’ past experience and behaviours to better predict such candidates’ success at work. In this regard, job candidates/workers should prepare themselves to possess these essential work skill sets so that they can be successful in the business arena and should understand potential selection tools that firms may apply to recruit and select them.

This paper provides social/policy implications for the government and/or relevant public agencies of Thailand and of other EMEs. These governments should encourage firms across industries to invest resources in training and developing their employees/workers to possess those essential work skill sets so that these employees/workers are industry-ready, leading to the alleviation of the problems of skill and mismatch in the labour market.

This paper contributes to the prior literature on human resource management (HRM), the comparative institutional perspective on employment systems based on the varieties of capitalism (VoC) framework and the LPT of work in the following ways: first, this paper fills in the research gap in the field of HRM that calls for studies that explore how the COVID-19 pandemic shapes essential skill sets and skills development among workers within firms (Cooke et al., 2021). Second, this paper provides implications for the LPT of work regarding how essential work skill sets are likely to return the power of negotiation from employers to workers in EMEs during the current situation. Third, the VoC framework tends to focus on only two types of economies, liberal market economies and coordinated market economies. However, this paper examines essential work skill sets and potential screening tools in the context of the underresearched country of Thailand, an EME. In fact, the Thai labour market is quite different from that of other EMEs labour markets, as it is impacted by an aging workforce. This paper contributes to the literature on comparative institutional perspectives on employment systems as it redefines essential work skill sets, proposes various components of these skill sets among workers and examines potential selection tools that are applied across firms located in EMEs.

The effects of country-of-origin (COO) cues on product evaluations are well documented. However, research on the relative effects of COO compared to other geographical indicators, such as region-of-origin (ROO), on food purchases is still limited. This study investigates how geographical origin labels influence consumers' perceptions of product value and authenticity of foreign food, as well as subsequent purchase intention (PI) and willingness to pay premium prices (WTPPP). The moderating role of health consciousness on these relationships is also examined due to the coronavirus disease 2019 (COVID-19) pandemic.

This study uses a between-subjects experimental design conducted with 300 middle- and high-income Chinese consumers aged between 25 and 50 years. Hypotheses were tested using structural equation modelling.

Whilst under both COO and ROO cues, all five product values positively influenced consumers' WTPPP, only functional, economic and novelty values influenced PI. The ROO cue performed significantly better than the COO cue in eliciting functional, economic and novelty value perceptions, which triggered stronger PI and willingness to pay a premium price. These relationships were mediated by product authenticity (PA) and moderated by consumers' health consciousness (HC).

Because food labels provide salient product information that facilitates consumers' evaluation of products, marketers should assess which product value perceptions they wish to enhance and then choose the appropriate geographical indicators for their labelling strategies.

This study identifies the effects of COO and ROO cues on product values, authenticity, PI and WTPPP. It also provides valuable insights into the role of HC on consumers' purchase decisions, which also aids in understanding the impact of global crises on food purchases.

The purpose of this study was to examine consumer data acquired by branded prescription drug websites and the ethics of privacy related to the interconnected web of personal information accessed, packaged and resold by tracker technologies.

The research used the DMI Tracker Tool to collect data on the top 17 branded prescription drug websites, with a specific interest in the tracker technologies embedded in those websites. That data was analyzed using Gephi, an open-source data visualization tool, to map the network of trackers embedded in those branded prescription drug websites.

Findings visualize the interconnections between tracker technologies and prescription drug websites that undergird a system of personal data acquisition and programmatic advertising vehicles that serve the interests of prescription drug marketers and Big Tech. Based on the theory of platform ethics, the study demonstrated the presence of a technostructural ecosystem dominated by Big Tech, a system that goes unseen by consumers and serves the interests of advertisers and resellers of consumer data.

The 17 websites used in this study were limited to the top-selling prescription drugs or those with the highest ad expenditures. As such this study is not based on a random sampling of branded prescription drug websites. The popularity of these prescription drugs or the expanse of advertising associated with the drugs makes them appropriate to study the presence of tracking devices that collect data from consumers and serve advertising to them. It is also noted that websites are dynamic spaces, and some trackers within their infrastructures are apt to change over time.

Branded prescription drug information has over the past three decades become part of consumers’ routine search for information regarding what ails them. As drug promotion moved from print to TV and the Web, searching for drug information has become a part of everyday life. The implications of embedded trackers on branded prescription drug websites are the subject of this research.

This study has significant social implications as consumers who are searching for information regarding prescription medications may not want drug companies tracking them in a way that many perceive to be an invasion of privacy. Yet, as the Web is dominated by Big Tech, web developers have little choice but to remain a part of this technostructural ecosystem.

This study sheds light on branded prescription drug websites, exploring the imbalance between the websites under study, Big Tech and consumers who lack awareness of the system that operates backstage.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

There is always a need to discover how a paradox between a customer’s desire for a more personalized experience and their privacy and security concerns would shape their intention to continue using contactless payment methods. However, personalization–privacy paradox has not been well-covered over the area of contactless payment. Therefore, this study aims to empirically examine the impact of personalization–privacy paradox on the customers’ continued intention (CIN) to use contactless payment.

/methodology/approach – The empirical part of the current study was conducted in Saudi Arabia by collecting the primary data using online questionnaire from a convenience sample size of 297 actual users of contactless payment methods.

Based on structural equation modeling, personalization and privacy invasion were approved to significantly impact perceived value of information disclosure (PVD). Strong causal associations were confirmed between perceived severity, structural assurance and response cost with privacy invasion. Finally, both PVD and privacy invasion significantly predict CIN.

There are other important factors (i.e. technology interactivity, technology readiness, social influence, trust, prior experience, etc.) were not tested in the current study. Therefore, future studies would pay more attention regarding the impact of these factors. The current study data were also collected using a convenience sample of actual users of contactless payment methods. Therefore, there is a concern regarding the generalizability of the current study results to other kind of customers who have not used contactless payment.

This study has integrated both personalization–privacy paradox and protection motivation theory in one model. The current study holds value in providing a new and complete picture of the inhibitors and enablers of customers’ CIN to use contactless payment, including new types of inhibitors. Furthermore, personalization–privacy paradox has not been fully examined over the related area of Fintech and contactless payment in general. Therefore, this study was able to extend the theoretical horizon personalization–privacy paradox to new area (i.e. contactless payment) and new cultural context (Saudi Arabia).

The study investigates factors like media, tribal, religious and party politics' influence on Nigerias’ 2023 presidential election choice. It confirms dominant social media platforms and examines their influence on election polls, e-participation and political candidate choice. The main objectives of this study are to: investigate if tribal, religious and party politics affect the respondent’s choice of a presidential candidate, ascertain the respondent's most used social media platform for political engagement and determine how social media platforms influenced the election polls during the 2023 Nigerian presidential election.

A sample size of 384 registered voters was used to survey three states in Southeast Nigeria hinged on the technological acceptance model, the instrumentalist theory of ethnicity and the theory of reasoned action.

The study found that tribal politics did not influence political candidates during the 2023 Nigerian presidential election. However, religious and party politics influenced their choices as well as X (Twitter), found as the most used and most influential social media platform vital for enhancing participatory democracy and informing people at real-time.

The researchers experienced challenges such as ensuring that the respondents filled the questions appropriately to reduce the number of void questionnaires and a funding problem since they had yet to receive any grant to enhance the study.

The study commends improved Internet connectivity and accessibility among the citizens for increased political engagement on social media. It also recommends that the Nigerian government enforce the rule of law in politics to enable diverse tribes and religions to experience democratic e-participation and development without marginalisation or subjugation by incumbent power. The findings affirm that social media is apt in political communication during the 2023 presidential elections in Nigeria. The study is a contribution to knowledge, timely and original.

Inaccurate capturing and processing of customer requirements result in negative economic and ecological effects. Digital twins of customer demands promise to remedy these issues. However, successful implementation necessitates users' technology acceptance. This study contrasts three hierarchical digital twin levels with different degrees of user integration and examines determinants for their respective acceptance.

A structural equation model is applied in a comparative manner, considering different levels of digital twin radicalness. A multidimensional approach is used to measure attitudes towards usage. Data are collected in the context of organisational supply management.

Results show harmonious effects across digital twin levels. This indicates that technological radicality plays only a subordinate role when assessing acceptance determinants such as user perception on ease of use, usefulness, trust and risk.

Rather than focussing solely on technological factors, findings suggest that users prioritise the actual outcome and efficiency of the system. This perspective offers practical implications for organisations seeking to implement advanced systems and emphasises the significance of user perceptions beyond technological features.

The societal impact of this research are an appreciation of customer roles in the supply chain where an enhanced detection of customer needs and preferences aligns businesses with the dynamic and evolving demands of a diverse and a continuously environmentally-conscious consumer base.

This study applies a measurement model for technology acceptance in a unique and multidimensional manner. Thereby, a comparative analysis of user perceptions across different digital twin levels sheds more light on a nascent, promising and underexplored technological method. This interdisciplinary research combined knowledge from the supply chain management and management information systems fields by highlighting key factors for the adoption of complex technological methods.

This case explores how driver training school create experience value for their trainees. It describes the development of driver training industry, the foundation and new training mode of Rongan Driving School, changes and challenges of environment for Rongan facing and so on, which will guide readers to discuss six influence factors of customer experience, six dimensions of customer-experience value, the relationship between them, and the influence of social environment. Rongan's innovative training mode of “pay after learning, time-based billing, one car for one person”, provides a good training experience for driving trainees. It has become the benchmark of the national driving training industry within six years.