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In this chapter, we outline the evidence regarding the status effects of physical attractiveness in the workplace. We frame the effects of attractiveness as power and status dynamics shaped by the context of the workplace. Attractiveness serves as a social status that mostly provides benefits in the workplace, although the results are not uniformly positive. We first explain attractiveness as a status characteristic and how it operates in general social interactions. We then examine these effects across three typical yet significant career development milestones in a worker's life: (1) the selection and hiring process of employment, (2) day-to-day interactions with colleagues and co-workers and (3) in leadership roles and positions.

This study aims to identify and classify potential hospitality industry human resource (HR) risks in the luxury hotel context.

Data has been collected from five-star hotels in Antalya, one of the most popular tourism destinations in Turkey. The study conducted focus groups to examine how hotel managers and lower-level staff perceive such risks. Using these focus group discussions, the participants' perceptions of HR risks were explored and elicited.

Participating hotel managers' and employees' perceptions were utilized to build up an outline for examining the risks associated with human resources management (HRM) practices in hotels. The evaluation shows that HR risk types in luxury hotels vary highly and that each type of risk requires close examination.

Recognizing HR risks play a key role for hospitality industry leaders and managers in attaining their organizations' goals and objectives, offering a practical framework in identifying and governing their HR risks, allowing them to make better strategic choices regarding their HR risk management (RM) plans.

This study extends the hospitality literature by exploring new ways of identifying and classifying HRM risks in luxury hotels.

Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making.

In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method.

The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market.

In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness.

The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions.

The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position.

As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment.

The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements.

The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions.

The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions.

The authors introduce a new construct, reputational self-awareness (RSA). RSA represents the congruence between how individuals think they are viewed by others (i.e. metaperceptions) versus how they are actually viewed (i.e. other ratings). The authors sought to demonstrate that RSA is a superior predictor of performance indices.

Personality self-ratings from 381 business students and their ratings by 966 others were collected via online surveys. Other raters rated self-raters' personalities as well as their task performance, organizational citizenship behaviors (OCBs) and counterproductive work behaviors (CWBs).

Results indicate that RSA predicts variance in performance above and beyond self-report ratings, and performance is highest when metaperceptions and other ratings of performance are aligned. These results support the use of a multi-perspective approach to personality assessment as a useful tool for coaching and career development.

The authors' results support the use of a multi-perspective approach to personality assessment as a useful tool for coaching and career development. A cross-sectional design was used in which personality and performance data were gathered from respondents, and the P 720 is a relatively new personality instrument.

RSA is a valuable tool for employee development, coaching and counseling because, as extant research and the authors' findings demonstrate, awareness of how others view and judge one, one's reputation is essential information to guide work behaviors and career success. Therefore, a key career-development goal for trainers and counselors should be to use a multi-perspective approach to maximize clients' RSA.

Use of other ratings as opposed to traditional self-rating of personality provides superior prediction of behavior and is more useful for career development.

This is the first study to demonstrate utility of RSA, i.e. that individuals who more accurately assess their personality are rated as performing better by others. The authors' results offer new insights for personality research and career development and support the use of personality assessment from multiple perspectives, thus enabling the exploration of potentially insightful research questions that cannot be examined by assessing personality from a single perspective.

The purpose of this study was to investigate the effect of a facial photo of female job applicants on employment decisions.

A laboratory or “controlled” experiment was conducted to examine the impacts of job applicants’ facial photos, job applicants' professional qualifications (education level and years of work experience), raters’ gender and college student raters’ academic year on employment-related decisions in a hypothetical situation in the Chinese context. A descriptive analysis and mixed-design factorial ANOVA were conducted.

Significant main effects were found for the applicant’s photo and professional qualifications in the analysis of employment decisions. Significant interaction effects were also found in the analysis.

Discrimination against applicants who attached unattractive photos was more pronounced for the interview decision when the applicants had low qualifications than when they had high qualifications. Although both male and female raters’ ratings of the interview, hiring and salary decisions decreased as attractiveness decreased, it was more pronounced for male raters than female raters.

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 aim of this study is to analyze whether or not the interaction between academicians' cultural and social capitals has effects on their individual work performance.

A structural model was developed in the study to test the correlations between cultural capital, social capital and individual work performance. The data of the study were collected from 2,855 academicians.

The findings of the study indicate that both cultural and social capital has a simultaneous positive effect on individual work performance. It is also found that the cultural and social capital can account for 39% of the individual work performance and that social capital is a dominant driving force.

Although the cultural and social capital has significant effects on the individual work performance, these effects are not of casual nature.

Therefore, it is possible to argue that the cultural and social capital in higher education institutions should be encouraged. Future studies may employ samples of individuals to see whether not these effects have causal characteristics.

The findings of the study contributed to the existing knowledge on the work performance describing the new correlations among the patterns.

Although (tele)work is increasingly being adopted and employees’ behavior is recognized to be key for organizational outcomes (e.g. performance), the current literature on counterproductive work behaviors (CWB) in telework settings is fragmented, and a valid measurement instrument is lacking. This study aims to address this gap and starts by presenting a review of the current literature on counterproductive work behavior in flexible work arrangements (i.e. telework). Based on this study, eight categories of work behavior that appear to be frequent under telework settings were identified.

Next, four studies aimed at developing and validating a measurement instrument that captures employee counterproductive work behavior when teleworking: the counterproductive [tele]work behavior scale (CTwBS).

In Study 1, the CTwBS was created, and in Study 2, its factorial validity was examined (N = 350). In Study 3, using a sample of teleworkers (N = 289), the convergent and discriminant validity of the CTwBS was tested using self-ratings of (positive and negative) affect, attitudes toward telework and frequency of CWB in general. In Study 4, a daily-diary study across five workdays (N = 232 × 5 = 1,160) examined the criterion validity of the CTwBS.

The results indicated that the CTwBS is a valid and reliable instrument for capturing employee counterproductive work behavior in telework settings. Theoretical and practical implications are discussed.

To the best of the authors’ knowledge, CTwBS is the first measure aimed at assessing counterproductive work behavior in telework settings.

With the increase in the adoption of artificial intelligence (AI)-based decision-making, organizations are facilitating human–AI collaboration. This collaboration can occur in a variety of configurations with the division of labor, with differences in the nature of interdependence being parallel or sequential, along with or without the presence of specialization. This study intends to explore the extent to which humans express comfort with different models human–AI collaboration.

Situational response surveys were adopted to identify configurations where humans experience the greatest trust, role clarity and preferred feedback style. Regression analysis was used to analyze the results.

Some configurations contribute to greater trust and role clarity with AI as a colleague. There is no configuration in which AI as a colleague produces lower trust than humans. At the same time, the human distrust in AI may be less about human vs AI and more about the division of labor in which human–AI work.

The study explores the extent to which humans express comfort with different models of an algorithm as partners. It focuses on work design and the division of labor between humans and AI. The finding of the study emphasizes the role of work design in human–AI collaboration. There is human–AI work design that should be avoided as they reduce trust. Organizations need to be cautious in considering the impact of design on building trust and gaining acceptance with technology.

The paper's originality lies in focusing on the design of collaboration rather than on performance of the team.