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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.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

Fortification-interdiction models provide system designers with a broader perspective to identify and protect vital components. Based on this concept, the authors examine how disruptions impact critical supply systems and propose the most effective protection strategies based on three levels of decision-makers. This paper aims to investigate location and fortification decisions at the first level. Moreover, a redesign problem is presented in the third level to locate backup facilities and reallocate undisrupted facilities following the realization of the disruptive agent decisions at the second level.

To address this problem, the authors develop a tri-level planner-attacker-defender optimization model. The model minimizes investment and demand satisfaction costs and alleviates maximal post-disruption costs. While decisions are decentralized at different levels, the authors develop an integrated solution algorithm to solve the model using the column-and-constraint generation (CCG) method.

The model and the solution approach are tested on a real supply system consisting of several hospitals and demand areas in a region in Iran. Results indicate that incorporating redesign decisions at the third level reduces maximum disruption costs.

The paper makes the following contributions: presenting a novel tri-level optimization model to formulate facility location and interdiction problems simultaneously, considering corrective measures at the third level to reconfigure the system after interdiction, creating a resilient supply system that can fulfill all demands after disruptions, employing a nested CCG method to solve the model.

This science mapping analysis aims to discern current, emerging and future trends of pro-environmental behavior and the theory of planned behavior (TPB).

Bibliometric analysis through bibliographic coupling and co-word analysis were used to reveal the progress of this phenomenon. Of the 1,120 documents search in Web of Science (WoS) database, 1,031 were used in this analysis after restricting to journal publications and studies after the year 2000.

The results show that four themes emerged, namely the fundamentals of TPB for pro-environmental behavior, antecedents of pro-environmental behavior, integration of TPB with the norm activation model and value belief theory and studies of pro-environmental behavior in developing countries. Environmental concern, environmental awareness, environmental knowledge and environmental education were the most commonly integrated variables.

This research is unique in the sense that the integration between TPB and other prominent theories of pro-environmental behavior is vital to predict individual pro-environmental behavior and understand the fundamental scientific importance of the domain. The norm activation model has been integrated with TPB in many pro-environmental behaviors. Mainstream media stakeholders should design and implement a plan for strategic communication and awareness campaigns in the community to encourage consumers to engage in many behaviors that lead to environmental sustainability.

This study presents a science mapping approach to uncover crucial knowledge structure related to pro-environmental behavior and the theory of planned behavior.

From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.

This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.

The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.

This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

Globally, the halal cosmetics market is experiencing rapid growth and is considered a key economic driver in shaping economy development and growth. However, the extant research on halal cosmetics is fragmented, potentially impeding the field’s advancement when challenged with conflicting viewpoints and limited replications. Therefore, this paper aims to address the knowledge gap by conducting a rigorous and technology-enabled systematic review by leveraging appropriate software to comprehensively evaluate the state of the halal cosmetics literature.

A domain-based review using a hybrid approach that incorporates both bibliometric and interpretive analyses are used to comprehensively assess the current progress of halal cosmetics, identify research gaps and suggest potential directions for future research.

Through a comprehensive review of 66 articles, this review provides a holistic and comprehensive overview of halal cosmetics that both academic scholars and market practitioners can rely upon in strategizing and positioning for future development of halal cosmetics. The study provides a holistic and comprehensive overview of halal cosmetics that both academic scholars and market practitioners can reply upon in strategizing and positioning for future development of halal cosmetics.

The fragmented knowledge of extant research on halal cosmetics across various disciplines limits a comprehensive understanding of the field. It is opportune to conduct a comprehensive and systematic review of the field, providing insight into both its current and future progress. In this regard, this review serves as a “one-stop reference” in providing a state-of-the-art understanding of the field, and enables industry practitioners to reveal the full potential and bridge the theory-practice gap in the halal cosmetics industry.

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

The rise of communication software has changed our work style. The objectives of this study are: (1) to explore the effect of supervisors making after-hours work requests using communication software (SWRUCS) on employees’ job stress, quality of life and (2) to examine the moderating effect of personality traits and the cross-level contextual effect of social support.

A questionnaire survey was conducted to obtain information from 357 employees.

The results suggested that SWRUCS exacerbated job stress, which negatively impacted on quality of life and well-being. Moreover, different personality traits can either increase or decrease the positive or negative effect of SWRUCS on job stress. This study also revealed that social support can reduce employees’ job stress in a cross-level fashion. Furthermore, social support, especially organizational and supervisory support, can decrease the negative effect of job stress on employees’ quality of life and well-being.

Theoretically, this study has broadened the research scope of the organizational application of communication software, and practically, this study has demonstrated the reason why organizations should provide social support and select employees with suitable personality traits.

This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.

The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.

The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.

The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.

By integrating the conservation of resources and the emotion-as-social-information theories, this study aims to question whether the leader’s effort to calm down when team members perceive intra-team conflict (ITC) may have a counterproductive effect on their interpersonal functioning. Specifically, the authors investigated whether team members with higher individual perceptions of ITC would be more likely to experience interpersonal strain (ISW) when their team leaders downregulate or suppress their emotional responses (i.e. high interpersonal modulation of emotional responses [MER]). A further objective of the study was to examine whether this exacerbating effect would be conditional on the leader’s sex.

Data were collected from 236 white collars nested in 48 teams (Msize = 6.23; SDsize = 2.69) and their respective team leaders (56.7% men) of a large organization providing financial services.

Multilevel model results showed that team members confronted with higher ITC experienced higher ISW levels, especially when the leader’s interpersonal modulation of team members’ emotional responses was high (vs low). This effect was stronger when the interpersonal modulation was enacted by women (vs men) team leaders.

This study moves an important step forward in the conflict and ISW literature, as it is the first to identify a leader’s MER and sex as key boundary conditions under which ITC is related to team members’ ISW. The implications of these findings for theory and practice are discussed.