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The “what is beautiful is good” (WIBIG) effect implies that observers tend to perceive physically attractive people in a positive light. The authors investigate how the WIBIG effect applies to user personas, measuring designers' perceptions and task performance when employing user personas for the design of information technology (IT) solutions.

In a user experiment, the authors tested six different personas with 235 participants that were asked to develop remote work solutions based on their interaction with a fictitious user persona.

The findings showed that a user persona's perceived attractiveness was positively correlated with other perceptions of the persona. The personas' completeness, credibility, empathy, likability and usefulness increased with attractiveness. More attractive personas were also perceived as more agreeable, emotionally stable, extraverted and open, and the participants spent more time engaging with personas they perceived attractive. A linguistic analysis indicated that the IT solutions created for more attractive user personas demonstrated a higher degree of affect, but for the most part, task outputs did not vary by the personas' perceived attractiveness.

The WIBIG effect applies when designing IT solutions with user personas, but its effect on task outputs appears limited. The perceived attractiveness of a user persona can impact how designers interact with and engage with the persona, which can influence the quality or the type of the IT solutions created based on the persona. Also, the findings point to the need to incorporate hedonic qualities into the persona creation process. For example, there may be contexts where it is helpful that the personas be attractive; there may be contexts where the attractiveness of the personas is unimportant or even a distraction.

The findings point to the need to incorporate hedonic qualities into the persona creation process. For example, there may be contexts where it is helpful that the personas be attractive; there may be contexts where the attractiveness of the personas is unimportant or even a distraction.

Because personas are created to closely resemble real people, the authors might expect the WIBIG effect to apply. The WIBIG effect might lead decision makers to favor more attractive personas when designing IT solutions. However, despite its potential relevance for decision making with personas, as far as the authors know, no prior study has investigated whether the WIBIG effect extends to the context of personas. Overall, it is important to understand how human factors apply to IT system design with personas, so that the personas can be created to minimize potentially detrimental effects as much as possible.

In the context of new workplace environment, this study aims to study and generate insights about artificial intelligence (AI) adoption in hiring process of firms. It is very relevant when AI is dramatically reshaping hiring function in the changing scenario.

The objectives are achieved with the help of three studies involving Delphi method to explore the criteria for AI adoption decision. Followed by two multi criteria decision-making techniques, i.e. analytic hierarchy process to identify weights of the criteria and fuzzy technique for order preference by similarity to ideal solution to assess the extent of AI adoption in hiring.

The findings reveal that information security and return on investment are considered two very important criteria by human resources managers while contemplating the adoption of AI in hiring process. It was found that AI adoption will be suitable at the sourcing and initial screening stages of hiring. And the suitability of the hiring stage where AI can be applied has been found to have changed from before and after the onset of COVID-19 pandemic situation. The findings and its discussion assist and enhance better decisions about AI adoption in hiring processes of firms amid changing scenario – external and internal to a firm.

Findings also highlight research implications for future research studies in this emerging area.

Results act as a starting point for other human resources managers, who are still pondering over the idea of adopting AI in hiring in future.

This paper through a systematic approach contributes by identifying important evaluation criteria influencing AI adoption in firms and extent of its application in the stages of hiring. It makes a substantial contribution to the under-developed yet emerging paradigm of AI based hiring in practice and research.

This paper aims to uncover the significance of capacity, capacity utilization (CU) and its role in the quality and productivity improvement in an industrial environment. Besides, the current study is also aiming to explore the various ways to estimate CU and its status across the world.

In the present study, a comprehensive literature review on capacity and CU is carried out to expose the research direction in the field of CU. This work is primarily focused on capacity, CU and their estimation methods based on the research in various industries of different countries and current status in present scenario across the world.

The literature reveals that CU estimation is carried out by some government/central agencies at the national or sector level rather than the industry level in most of the productive nations. As far as industrial growth is concerned, capacity management should be carried out at a particular industry level so that engineering managers can be able to find out loopholes for huge capacity waste within the plant. It is observed that CU in the industrial sectors mainly computed by time series method, survey method, economic approach and engineering approach worldwide.

This paper tries to cover almost all research work in the field of CU in various industrial sectors. However, the organizations which are producing the product with limited demand may get benefit inadequately.

This paper provides a vision to management toward productivity improvement through optimal utilization of available resources. As in most organizations, CU issues are much neglected areas.

This paper provides valuable insights on capacity and CU in the industrial sector across the world. Besides, it focused on comprehensive literature of capacity and various methods to estimate CU in industrial sectors.

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).

A semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.

The review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.

Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

The purpose of this paper is to develop and validate an empirical model examining the job seekers’ perception about information quality of corporate employment websites and its impact on their attitude toward the websites through perceived playfulness and usefulness. Furthermore, the study also examines the job seekers’ e-trust as condition under which these mechanisms generate website attitude.

A sample of 385 active job seekers was selected through systematic random sampling. A web-based questionnaire was used to elicit responses for the study. Structural equation modeling was used to validate the proposed model.

Results indicate that the information quality dimensions positively influence perceived playfulness and perceived usefulness, which in turn evoke the website attitude. Furthermore, e-trust was found to moderate the above said relationships.

The study contribution lies in an empirical validation of a model showing the mechanisms and the condition through which the relationship exists between perceived information quality of e-recruitment websites and job seekers’ website attitude, and thus responds to the call for additional research that generalizes the influence of information characteristics of websites on job seekers’ behavioral outcomes.

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

During the Covid-19 pandemic, people were deprived of their freedom, unable to engage in physical and social activities, and worried about their health. Uncertainty, insecurity, and confinement are all factors that may induce stress, uneasiness, fear, and depression. In this context, this study aims to identify possible relationships of emotions caused by health risks and restrictions to outdoor activities with well-informed decisions about food consumption.

The theoretical framework of this research draws on the stimulus-organism-response paradigm yielding six research hypotheses. An online survey was designated to test these hypotheses. A total of 1,298 responses were gathered from Italy, Greece, and the United Kingdom. Data analyses include demographic group comparisons, moderation, and multiple regression tests.

The results showed that when people miss their usual activities (including freedom of movement, social contact, travelling, personal care services, leisure activities, and eating at restaurants) and worry about their health and the health of their families, they turn to safer food choices of higher quality, dedicating more of their time and resources to cooking and eating.

The findings showcase how risk-based thinking is critical for management and marketing strategies. Academics and practitioners may rely on these findings to include extreme conditions within their scope, understanding food literacy as a resilience factor to cope with health risks and stimulated emotions.

This study identified food behavioural patterns under risk-laden conditions. A health risk acted as an opportunity to look at food consumption as a means of resilience.

This review aims to evaluate and review literature published in the area of rising concerns that red meat consumption may be associated with risk of type 2 diabetes mellitus (T2DM), although there have been discrepancies between study findings, and put the findings into context.

Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature review was undertaken to locate and summarise relevant studies which included epidemiological and clinical studies published between 2004 and 2014.

A total of 23 studies were found, with 21 epidemiological and two clinical studies meeting the criteria. Overall, the totality of the evidence indicates that while processed meat consumption appears to be associated with T2DM risk, the effect is much weaker for red meat, with some associations attenuated after controlling for body weight parameters. Where studies have considered high intakes in relation to T2DM risk, meat intake has tended to exceed 600 g per week. Therefore, keeping red meat intakes within recommended guidelines of no more than 500 g per week, while opting for lean cuts or trimming fat, would seem to be an evidence-based response.

The majority of studies conducted to date have been observational cohorts which cannot determine cause and effect. Most of these used food frequency questionnaires which are known to be subject to misclassification errors (Brown, 2006). Clearly, more randomised controlled trials are needed to establish whether red meat consumption impacts on markers of glucose control. Until then, conclusions can only be viewed as speculative.

This paper provides an up-to-date systematic review of the literature, looking at inter-relationships between red meat consumption and T2DM risk.

Rare earths are essential materials for many high-tech industries critical to both economic development and national defense. China, the world's dominant supplier of rare earths, has recently been imposing stricter controls over its production and export. The purpose of this paper is to examine the domestic roots of the changes in China's rare earth industry production and exports in its three-decade rise to the current global monopoly.

This paper adopts the historical institutionalism approach to analyze the trajectory of industry and trade development. The author analyzes data collected from government whitepapers and reputed scholarly and news sources.

This paper argues that the Chinese rare earth industry has gone through three periods of development, in which the state attempted to control the market and industry through reformulating rules and institutions to achieve state goals. Domestic state institutions, combined with macroeconomic environment and state governance strategy shaped the three-decade experience of rare earth industry and trade development in China.

This paper builds on existing findings about Chinese state regulations to provide a novel analytical framework to analyze the role of the state in industry and trade development in the rare earth industry. The focus on a single strategic industry seldom studied in the current literature also provides ample empirical value to further scholarly understanding about this industry.