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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 purpose of this study is to examine the influence of conscientiousness on entrepreneurship over and above the impact of other factors that are associated with entrepreneurship in the literature.

The design uses household responses from the Panel Study of Income Dynamics (PSID) biennial survey that follows the same heads of households over time to measure their conscientiousness, businesses owned and other demographic and financial characteristics. Ordinary least squares (OLS), Probit and Poisson regression techniques are applied at the head of household and state level to examine the relationship.

The results show heads of households’ conscientiousness positively relating to the average number of businesses owned, beyond other Big Five traits and the impact of other characteristics. A one-standard deviation increase in conscientiousness is significantly associated with a 0.012 increase in the number of businesses owned. This association is robust to alternative regression specifications and variable measurements.

The results are original to the finance literature, complementing studies by linking intrinsic head of household-level traits to entrepreneurship while controlling for external financial and demographic factors. The study also attempts to externally validate previous findings using aggregate-level outcomes. The data and setting used to measure personality traits as well as entrepreneurial outcomes are original to the entrepreneurship literature, validating previous findings.

Employee Stock Options [ESOs] have been used widely as a component of employees' compensation. To maximise the incentive effect of these options it is very important to understand the exercise decision of the employees. This is an important financial decision that is dependent on both rational and psychological factors. This paper aims to study the mediating role of Herding Bias on Personality Traits and the employees' decision to exercise ESOs.

The data were collected through a self-structured questionnaire from 210 employees of Banks and NBFCs [Non-Banking Financial Companies] who have received and exercised the ESOs. SPSS MACRO version 25 was used to understand the mediational effect of Herding Bias on Personality Traits and Employees' decision to exercise their ESOs.

The results showed that Personality Traits affect the employees' decision to exercise their ESOs. The study also shows a partial negative mediating effect of Herding Bias on Personality Traits and employees' decision to exercise ESOs.

Limited study has been conducted on how the employees make their decision to exercise ESOs. Although extant studies have touched upon the importance of including behavioural biases in ascertaining the exercise decision of the employees, the predictors of the behavioural biases have not been studied under this context. To the best of the author's knowledge, this study is the first in itself to study the inter-linkage between Personality Traits, Herding Bias and employees' decision to exercise ESOs.

To date, to the best of the authors’ knowledge, the use of implicit measures in the service research domain is limited. This paper aims to introduce implicit measures and explain why, or for what purpose, they are worthwhile to consider; how these measures can be used; and when and where implicit measures merit the service researcher’s consideration.

To gain an understanding of how implicit measures could benefit service research, three promising implicit measures are discussed, namely, the implicit association test, the affect misattribution procedure and the propositional evaluation paradigm. More specifically, this paper delves into how implicit measures can support service research, focusing on three focal service topics, namely, technology, affective processes including customer experience and service employees.

This paper demonstrates how implicit measures can investigate paramount service-related subjects. Additionally, it provides essential methodological “need-to-knows” for assessing others’ work with implicit measures and/or for starting your own use of them.

This paper introduces when and why to consider integrating implicit measures in service research, along with a roadmap on how to get started.

Intellectual capital (IC) plays a crucial role in today’s volatile business landscape, yet its measurement remains complex. To better navigate these challenges, the authors propose the Integrated Intellectual Capital Measurement (IICM) model, an innovative, robust and comprehensive framework designed to capture IC amid business uncertainty. This study focuses on IC measurement models, typically reliant on secondary data, thus distinguishing it from conventional IC studies.

The authors conducted a systematic literature review (SLR) and bibliometric analysis across Web of Science, Scopus and EBSCO Business Source Ultimate in February 2023. This yielded 2,709 IC measurement studies, from which the authors selected 27 quantitative papers published from 1985 to 2023.

The analysis revealed no single, universally accepted approach for measuring IC, with company attributes such as size, industry and location significantly influencing IC measurement methods. A key finding is human capital’s critical yet underrepresented role in firm competitiveness, which the IICM model aims to elevate.

This is the first SLR focused on IC measurement amid business uncertainty, providing insights for better management and navigating turbulence. The authors envisage future research exploring the interplay between IC components, technology, innovation and network-building strategies for business resilience. Additionally, there is a need to understand better the IC’s impact on specific industries (automotive, transportation and hospitality), Social Development Goals and digital transformation performance.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

This study aims to explore and empirically examine an integrative model of the customer revenge process by linking two well-established theories of self-determination theory (SDT) and appraisal theory.

A survey of 901 respondents, followed by a post-hoc survey of 712 individuals, was conducted to examine the autonomous versus controlled orientations for revenge motivation.

The results show that customers’ orientation of motivation (OM) can regulate their revenge behavior (direct versus indirect) in case of service failures. Specifically, the interaction of OM components (i.e. autonomy, relatedness and competence) can play a significant role in the relationship between revenge predictors and revenge behavior. Implications for research and practice are discussed.

This paper offers a novel conceptual framework to explain the moderating effects of OM on the relationship between revenge predictors and revenge behavior. This study extends the application of SDT to the context of customer anger and revenge.

Although commitment and employability are legitimized in the current world of work, they also have a dark side that has been ignored in the extant literature. To tackle this gap, the study developed and examined a comprehensive theoretical framework including learning, motivation, commitment, employability and self-exploitation. Limited research exists that explicitly examines this relationship or explores its potential implications. The author theorizes employability as a cultural fantasy that ends up in self-exploitation.

The study concretizes Lacan's (1977; 1981 and 1988) psychoanalysis, utilizing a sample of 658 subjects from eight industries. The hypothesized relationships were examined using structural equation modeling (SEM) in AMOS.

The findings provided support for the hypothesized relationships. Employability escorts to self-exploitation. Those employees who try to remain relevant to their firms continue to engage in employability activities end up being exploited in this process.

The study provides a new roadmap to scholars of employability who wish to explore the domain further.

The theoretical knowledge from this research will inform practice. It will influence managers and policymakers in the organization as well as politicians. Although the macroaspects of the organizational environment are beyond the control of an organization, the development efforts of the organization should be real and should not estrange individuals from their true nature. The real intent should be to unite the individual with its true nature. This way, it will be real development and will empower individuals rather than exploitation.

The finding that commitment is linked to self-exploitation via employability has implications for managers and policymakers. To avoid estrangement and exploitation, the organization should focus on employee real development. To have an ideal workplace, where employees unite with their nature, the organization should invest in employees, focus on their real needs, emphasize their career prospects and constantly provide them with learning and growth opportunities. In addition to material compensation, the organization should connect people with their true spirit. An organization that is concerned with people's real needs and real development will have a pool of human capital that will create real value for the organization and society as well.

The dark side of employability has been ignored in the extant literature. Limited research exists that explicitly examines this relationship or explores its potential implications. This study is an initiative for such debate.

This study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.

This study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.

Experimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.

The novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.