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The purpose of this paper, a critical reflection, is twofold. First, by comprehensively reviewing scale development procedures in hospitality research, a concerning lack of nomological validity testing is demonstrated. Second, the need for nomological validity testing is discussed and both conceptually and empirically reasoned.

This research systematically reviews scale development studies in three leading hospitality journals, including Cornell Hospitality Quarterly, International Journal of Contemporary Hospitality Management and International Journal of Hospitality Management over ten years (2012–2021) to analyze the completeness of scale development procedures. Specifically, the authors evaluate whether the reviewed studies engage in testing the nomological and predictive validity of the newly developed measures.

The results indicate a concerning gap in the current practices in hospitality research. Specifically, only 33.3% of the examined studies assess nomological validity. These findings collectively underscore the need for improving the comprehensiveness of scale development processes in hospitality research.

The study offers important implications for hospitality researchers. The paper provides an extensive discussion on the importance and benefits of testing for nomological validity in scale development studies, contributing to the completeness and consistency of scale development procedures in the hospitality discipline.

This research critically assesses prevalent, and widely accepted, scale development procedures in hospitality research. This research empirically demonstrates the neglect of nomological validity issues in scale development practices in hospitality research. Scale development is an essential scientific practice used to create a research instrument in a field of study, improving our understanding of a specific phenomenon and contributing to knowledge creation. Considering the significance of scale development in advancing the field of hospitality research, the validation procedures involved in the scale development processes are of utmost importance and should be thoroughly applied.

This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

This study aims to investigate the direct effect of intellectual capital on the organizational performance of Indonesian state-owned enterprises (SOEs) and their subsidiaries. Furthermore, it also examines whether the relationship is mediated by open innovation and moderated by organizational inertia.

This study is designed as quantitative research. A survey method is employed to collect data by distributing questionnaires to the upper-level managers of the SOEs and their subsidiaries. A total of 293 questionnaires were distributed to the respondents, and 97 responses were obtained for further analysis. The partial least square structural equation modeling (PLS-SEM) is used to test the hypotheses. A mediation-moderation research framework is employed.

The results show that intellectual capital has a positive effect on organizational performance. Further results also demonstrate that open innovation mediates the intellectual capital–organizational performance relationship and organizational inertia moderates the intellectual capital–organizational performance relationship. Theoretically, the findings contribute to the resource-based view (RBV) and knowledge-based view (KBV) by providing empirical evidence of the importance of distinctive internal resources in achieving superior organizational performance. Practically, the findings provide strategic information for managers that they should properly manage intellectual capital, open innovation and organizational inertia because of their effects on organizational performance.

First, this study addresses the previous research gaps by confirming that intellectual capital has a positive effect on organizational performance in the research setting of an emerging market. Second, by using a mediation research framework, this study shows that open innovation mediates the relationship between intellectual capital and organizational performance. Third, by using a moderating research framework, this study also reveals that organizational inertia weakens the relationship between intellectual capital and organizational performance. Those associations are rarely researched.

This study analyses configurations of national culture as boundary conditions of countries’ national systems of innovation (NSI). Drawing from the NSI approach, we argue that culture’s role is that of a contingency factor shaping the relationship between investments in innovation and national innovation outputs.

We assessed the moderation effect of national culture through a systematic, two-stage approach using fuzzy-set Qualitative Comparative Analysis (fsQCA), which allows the analysis of changes induced by the moderator variables. Analyses were conducted with a diverse sample of 61 countries over a period spanning 12 years, from 2011 to 2022.

Findings reveal that investments in innovation, but not individual cultural dimensions, is a necessary condition for high innovation outputs. Furthermore, several configurations of cultural dimensions were identified as moderators of the relationship between investments in innovation and innovation outputs.

This study provides insights into cross-national innovation research by exposing the role of cultural configurations, rather than just individual cultural dimensions, as boundary conditions involved in the achievement of high levels of innovation.

Project-based organizations (PBOs) face challenges to enhance employee work engagement because of dynamic and constant role configuration. Accordingly, this study aims to explore how ethical leadership enhances employee work engagement from a sensemaking perspective.

This study used a questionnaire-based quantitative research design to collect data from 194 full-time employees in PBOs. The data were analyzed via partial least squares-structural equation modeling (PLS-SEM) technique to test hypotheses.

The findings show a positive relationship between ethical leadership and work engagement. Additionally, the relationship between ethical leadership and work engagement is mediated by two sensemaking mechanisms, i.e. goal commitment and prosocial.

This study deepens the understanding of how ethical leadership enhances work engagement in PBOs by providing two sensemaking mechanisms. By exploring the sensemaking process through which ethical leaders help employees construct identity, the findings contribute to the current literature on how ethical leadership enhances work engagement in PBOs.

The purpose of this study is to empirically explore how firms configure centrifugal and centripetal forces in promoting breakthrough innovation (BI), thus improving their strategic performance (SP) in the artificial intelligence (AI) context.

This study applies the centrifugal and centripetal forces model to a survey sample of 285 Chinese AI firms. Fuzzy-set qualitative comparative analysis (fsQCA) and propensity score matching (PSM) are integrated to explore the configurational effects of three centrifugal forces—the autonomy of technical experts, knowledge search and alliance network—and two centripetal forces—strictness of organisational institutions (SOI) and human–human–AI collaboration (HHAC)—on BI, examining whether the configurations that enhance BI can further improve SP.

The results indicate that the strictness of innovation institutions (SII) and strictness of ethical institutions (SEI) are equally important for determining SOI. Three configurations can improve BI when SOI and HHAC are the core conditions; only one of three configurations can further improve SP significantly.

By introducing SOI composed of equally important levels of SII and SEI and HHAC, this research is one of the few empirical studies to explore the mechanisms behind the impact of centrifugal and centripetal forces on BI and SP, which may help researchers and managers address innovation challenges in the AI context.

This study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).

Based on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.

LSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.

This study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.

Drawing on resource and capability theory, this study aimed to analyze the relationship between the dynamic capabilities (DC), the knowledge management (KM) process (KMP) and results in customers and people. More specifically, the study argues that the KM process mediates the relationship between DC and the results outlined above. In addition, a predictive analysis is carried out that demonstrates the relevance of the KM process in the model.

The study sample is made up of 118 Spanish organizations that have some kind of recognition of excellence awarded by the European Foundation for Quality Management (EFQM). Partial least squares methodology is used to validate the research model, the hypothesis testing and the predictive analysis.

The results show that organizations which leverage the DC through the KMP improve customer and people outcomes. Moreover, the predictive power is higher when the KMPmediates the relationship between the DC and the results.

There is no consensus in the literature on the relationship between DC, KM and performance. Moreover, there are also not enough papers that study KM or DC through the dimensions that define these constructs or variables. Given this need, this work considers the KMP according to the stages of knowledge creation, storage, transfer and application. Similarly, DC is dimensioned in sensing, learning, integrating and coordinating capabilities. These, as reconfigurators of knowledge assets, influence the KMP. Accordingly, the empirical model connects these knowledge domains and analyses their link to outcomes.

With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction.

In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting.

Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters.

This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction.