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Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.

The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.

Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.

These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.

This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

This book aimed to conceptualise a construction workforce management model suitable for effectively managing workers in construction organisations. To this end, this chapter presents the conceptualised model, which consists of seven workforce management practices with their respective measurement variables. Drawing from existing theories, models, and practices, the chapter concludes that a construction organisation that will attain its strategic objectives in the current fourth industrial revolution era must be willing to promote effective recruitment and selection, compensation and benefits, performance management and appraisal, employee involvement and empowerment, training and development, as well as improving workers emotional intelligence and handling external environment pressure. These practices can promote proactiveness, participation, and improved skills and can lead to effective commitment, better quality, and flexibility within the organisation.

This study aims to examine the influence of two significant product labelling strategies (geographical indication [GI] vs country-of-origin [COO]) on shaping customer product attitude and purchase likelihood, considering consumers’ ethnocentric and cosmopolitan tendencies. The authors also investigate the boundary conditions and intervening mechanisms to manage the adverse consumer product evaluations and present mitigating procedures which reinstate favourable product evaluations and purchase likelihood.

The collected data from these all these studies were analysed using ANOVA and mediation anlaysis. The study tests the proposed hypotheses using three follow-up experimental investigations.

The study found that GI (vs COO) labels have a more significant impact on customers’ product evaluation and likelihood of purchase and supported the dispositional effect of ethnocentric and cosmopolitan inclinations. Further, the results indicated that self-product congruence can efficiently regulate consumer dispositions. Also, the results confirmed the significant impact of product identification on influencing consumer attitudes.

The above-said insights add practical insights, particularly concerning product labelling. Also, the insights on product attitudes and purchase likelihood intricacies in the context of product labelling enable companies to comprehend better the significance of GI labels, COO labels and self-product congruence.

To the best of the authors’ knowledge, this is the first time a study has compared the role of two significant product labelling strategies (GI vs COO) in shaping customer product evaluations, confirmed its boundary conditions and shown how to transform them into helpful customer product outcomes.

The incentive cost of enterprises increases significantly with the rapid growth of the social commerce (SC) market. In this context, enterprises need to develop the optimal strategy to improve incentive effectiveness and reduce cost. Different types of consumers’ responses to incentives bring different values to enterprises. Hence, this paper proposes the social commerce value network (SCVN) to help enterprises study the contributions of different types of consumers to the network.

Based on the graphical evaluation and review technique (GERT), the authors construct the social commerce value GERT (i.e. SCV-GERT) network and design three progressive experiments for estimating the value contributions of “network stage”, “consumer type”, and “resource type” to the SCVN under the same incentives. The authors initialize the SCV-GERT model with consumer data in SC and distinguish the most valuable consumers by adjusting the incentive parameters.

The results show that the SCV-GERT model can well describe the value flow of SCVN. The incentive on forwarding consumers brings the greatest value gain to the SCVN, and social trust contributes the most to forwarding consumers.

Under the guidance of the results, platforms and enterprises in SC can select the optimal type of consumers who bring the maximum network value so as to improve the effectiveness of incentive strategy and reduce marketing costs. A four-level incentive system should be established according to the ranking of the corresponding value gains: forwarding consumers > agent consumers > commenting consumers > potential consumers. Enterprises also need to find ways to improve the social resource investments of consumers participating in SC.

This paper investigates the incentive problem in SC grounded in the SCVN and uses the GERT method to construct the SCV-GERT model, which is the first attempt to introduce GERT into the SC context. This study also makes up for the lack of comparative research on different types of consumers in SC and can provide support for enterprises’ customer relationship management and marketing decisions.

This study aims to reach a perception of the advance of research on deviant organisational behaviours.

This research has been done through the text mining method. By reviewing, the papers were selected 360 papers between 1984 and 2020. Based on the Davis–Boldin index, 11 optimal clusters were gained. Then the roots were ranked in any group, using the Simple Additive Weighting technique. Data were analysed by RapidMiner and MATLAB software.

According to the results obtained, clusters are included leadership styles, job attitudes, spirituality in the workplace, work psychology, personality characteristics, classification and management of deviant workplace behaviours, service and customer orientation, deviation in sales, psychological contracts, group dynamics and inappropriate supervision.

This study provides a landscape and roadmap for future investigation on deviant organisational behaviours.

This study aims to understand how brand personality affects purchase intention through brand love and perceived quality in e-commerce.

Three hundred forty-eight users of e-commerce sites in New Delhi, India, were surveyed for the study. The data set was examined using confirmatory factor analysis, and the research hypotheses were assessed using structural equation modeling.

Two important conclusions emerged from the study. First, brand love and perceived quality have been considerably and favorably influenced by all six dimensions of brand personality of e-commerce brands. Second, the purchase intention toward the e-commerce sites is significantly and positively impacted by brand love and perceived quality.

This study by exploring various dimensions of brand personality, will assist e-commerce executives in increasing purchase intention toward the e-retailing sites.

This research is supposed to be the foremost to look at how brand personality, through brand love and perceived quality affects purchase intention toward e-commerce websites. The attachment theory is used in this study as a theoretical foundation for linking e-commerce brand personality to customers’ purchase intentions via brand love and perceived quality.

The study aims to propose a predictive model that combines personality and demographic factors to predict student academic performance (SAP). This research study works on understanding, enhancing and applying techniques to enhance the prediction of SAP.

The authors gathered information from 305 university students from Al-Zintan University Libya. The study uses a survey questionnaire to collect data on essential variables. The purpose of the questionnaire is to discover variables that affect students' academic performance. The survey questionnaire has 44 closed questions with Likert scale designs that were distributed to a variety of college students at the start of the first semester of 2022. It includes questions about demographics, personality, employment and institutional aspects. The authors proposed a predictive model to identify the main fundamental components, consisting of one dependent variable (SAP) and five independent constructs. The suggested model is tested using partial least squares (PLS) and structural equation modeling (SEM), which perform better than covariance-based structural equation modeling (CB-SEM). PLS-SEM performs well with smaller sample sizes, even for complicated models.

The study results show that the proposed model accurately predicted the student's academic performance. The personality trait variables are a key factor that determines the actual student's academic performance. The student's academic performance is significantly impacted by each variable in the personality trait variables as well.

The process of validating research was done empirically through the accuracy and efficiency of model performance. The study differs from previous studies in that it accumulated a wide range of factors from different dimensions, including student demographics and personality trait factors. The authors developed a structural equation model to predict students' academic performance.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

The purpose of the paper is to present an integrated methodology for identifying trends in a particular subject area based on a combination of advanced text mining and expert methods. The authors aim to test it in an area of clinical psychology and psychotherapy in 2010–2019.

The authors demonstrate the way of applying text-mining and the Word2Vec model to identify hot topics (HT) and emerging trends (ET) in clinical psychology and psychotherapy. The analysis of 11.3 million scientific publications in the Microsoft Academic Graph database revealed the most rapidly growing clinical psychology and psychotherapy terms – those with the largest increase in the number of publications reflecting real or potential trends.

The proposed approach allows one to identify HT and ET for the six thematic clusters related to mental disorders, symptoms, pharmacology, psychotherapy, treatment techniques and important psychological skills.

The developed methodology allows one to see the broad picture of the most dynamic research areas in the field of clinical psychology and psychotherapy in 2010–2019. For clinicians, who are often overwhelmed by practical work, this map of the current research can help identify the areas worthy of further attention to improve the effectiveness of their clinical work. This methodology might be applied for the identification of trends in any other subject area by taking into account its specificity.

The paper demonstrates the value of the advanced text-mining approach for understanding trends in a subject area. To the best of the authors’ knowledge, for the first time, text-mining and the Word2Vec model have been applied to identifying trends in the field of clinical psychology and psychotherapy.