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This paper aims to present a theoretical framework which reveals the relationship between job evaluation (JE) and the development of fair wage structure from the organizational justice (OJ) perspective. It focuses on analyzing the dimensions of job-based pay structure and the use of multifaceted construct of OJ (procedures, distribution and interaction) to determine how the perceived justice of JE's multi-levels construct contributes to achieve the fairness of wage structure.

This paper adopts an analytical descriptive approach in terms of explaining the perspectives and viewpoints related to the analysis. This paper is based on examining a theoretical framework provided by the authors based on a theoretical review of literature and a set of empirical evidences.

The design of a hierarchical wage structure counts on the multidimensional approach of JE which consists of three dimensions (processes, outcomes and social system). In addition, the determination of wage structure fairness is dependent on the assessment of the perceived justice of: JE's procedures, wages distribution and management's treatment with its employees.

This study provides a new theoretical contribution in studying the relationship between JE and the design of fair wage structure. This contribution can be regarded as a theoretical foundation for conducting some empirical and comparative studies in the future. The study affords directive mechanisms to policymakers in order to enhance the fairness of the wage structure across the state.

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.

Applying the institutional logics perspective to applicant attraction, this study investigates the level of uniformity among preferences for consulting job attributes associated with the institutional logics of the corporation, the profession and the family, and tests for the influence of anticipatory socialization differences.

The study uses a discrete choice experiment with 232 business students. A hierarchical Bayes approach to conjoint analysis uncovers part-worth heterogeneity and allows for subsequent cluster and regression analysis of the choice data.

The findings identify a dominant job-oriented preference type and a minor career-oriented preference type. Anticipatory socialization through personal prior work experience and the occupation of friends decreases adherence to the logic of profession and increases the relevance of the family logic. The parents' occupation has only a minimal influence on preferences.

The study provides attribute-based recommendations on how professional service firms can effectively address the complex expectations of potential applicants in their job ads for an entry position and underlines the role of intra-generational reference groups as important anticipatory socializers.

By testing individual socialization effects at the pre-hire stage and beyond the organizational level, the study fills a void in both the recruitment and the institutional literature.

Analytics technologies are profoundly changing the way in which organizations generate economic and social value from data. Consequently, the professional roles of data scientists and data analysts are in high demand in the labor market. Although the technical competencies expected for these roles are well known, their behavioral competencies have not been thoroughly investigated. Drawing on the competency-based theoretical framework, this study aims to address this gap, providing evidence of the emotional, social and cognitive competencies that data scientists and data analysts most frequently demonstrate when they effectively perform their jobs, and identifying those competencies that distinguish them.

This study is exploratory in nature and adopts the competency-based methodology through the analysis of in-depth behavioral event interviews collected from a sample of 24 Italian data scientists and data analysts.

The findings empirically enrich the extant literature on the intangible dimensions of human capital that are relevant in analytics roles. Specifically, the results show that, in comparison to data analysts, data scientists more frequently use certain competencies related to self-awareness, teamwork, networking, flexibility, system thinking and lateral thinking.

The study was conducted in a small sample and in a specific geographical area, and this may reduce the analytic generalizability of the findings.

The skills shortages that characterize these roles need to be addressed in a way that also considers the intangible dimensions of human capital. Educational institutions can design better curricula for entry-level data scientists and analysts who encompass the development of behavioral competencies. Organizations can effectively orient the recruitment and the training processes toward the most relevant competencies for those analytics roles.

This exploratory study advances our understanding of the competencies required by professionals who mostly contribute to the performance of data science teams. This article proposes a competency framework that can be adopted to assess a broader portfolio of the behaviors of big data professionals.

Data science promises new opportunities for organizational decision-making. Data scientists arguably play an important role in this regard and one can even observe a certain “buzz” around this nascent occupation. This paper enquires into how data scientists construct their occupational identity and the challenges they experience when enacting it.

Based on semi-structured interviews with data scientists working in different industries, the authors explore how these actors draw on their educational background, work experiences and perception of the contemporary digitalization discourse to craft their occupational identities.

The authors identify three main components of data scientists’ occupational identity: a scientific mindset, an interest in sophisticated forms of data work and a problem-solving attitude. The authors demonstrate how enacting this identity is sometimes challenged through what data scientists perceive as either too low or too high expectations that managers form towards them. To address those expectations, they engage in outward-facing identity work by carrying out educational work within the organization and (paradoxically) stressing both prestigious and non-prestigious parts of their work to “tame” the ambiguity and hype they perceive in managers’ expectations. In addition, they act upon themselves to better appreciate managers’ perspectives and expectations.

This study contributes to research on data scientists as well as the accounting literature that often refers to data scientists as new competitors for accountants. It cautions scholars and practitioners alike to be careful when discussing the possibilities and limitations of data science concerning advancements in accounting and control.

Previous literature has identified human resources as a key source of competitive advantage in organizations of all sizes. However, Small and Medium-sized Enterprises (SMEs) face difficulty in comprehensively implementing all recommended Human Resource Management (HRM) functions. In this study, we shed light on the field of HRM in SMEs by focusing on the context of Greek Small and Medium-sized Hotels (SMHs), which represent a dominant private sector employer across the country.

Using a fuzzy-set qualitative comparative analysis (fsQCA) and 34 in-depth interviews with SMHs' owners/managers, we explore the HRM conditions leading to high levels of performance, while taking into consideration the influence of internal key determinants.

We uncover three alternative successful HRM strategies that maximize business performance, namely the Compensation-based performers, the HRM developers and the HRM investors. Each strategy fits discreet organizational characteristics related to company size, ownership type and organizational structure.

To the best of the authors' knowledge this is among the first empirical studies that examine different and equifinal performance-enhancing configurations of HRM practices in SMHs.

The purpose of the study is to ascertain whether or not faculty members would be motivated to use e-Databases for research considering the impact of the Technology Acceptance Model2 (TAM2) cognitive instrumental processes of job relevance, output quality and result demonstrability.

The survey research design was applied. The selection of samples was based on a multistage sampling technique involving; purposive, simple/systematic random and total enumeration procedures. Five colleges and departments each were selected from the three universities that provided the setting for the conduct of this study, out of which a sample of 135 was drawn from the total population of 209. The questionnaire method was used for data gathering. Ninety-five percent return rate of the administered instrument was observed. Descriptive and inferential statistical tools were employed for data analyses.

Job relevance, output quality and result demonstrability are motivators of faculty use of e-Databases for research with result demonstrability wielding the strongest influence. Use of e-Databases for research is based on the usefulness level perceived of them. Faculty are highly predisposed to using the technology for research with the chances of getting published in reputable journal outlets ranked highest among other factors that influence faculty use of e-Databases.

The conceptualization of TAM2 cognitive instrumental processes as system characteristics and motivators of e-Databases use among faculty towards research engagement advances the understanding of intention to use e-Databases for research.