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The purpose of this paper is to analyze the occurrence of terms to identify the relevant topics and then to investigate the area (based on topics) of hospitality services that is highly associated with relationship quality. This research represents an opportunity to fill the gap in the current literature, and clarify the understanding of guests’ affective states by evaluating all aspects of their relationship with a hotel.

This research focuses on natural opinions upon which machine-learning algorithms can be executed: text summarization, sentiment analysis and latent Dirichlet allocation (LDA). Our data set contains 47,172 reviews of 33 hotels located in Las Vegas, and registered with Yelp. A component-based structural equation modeling (partial least squares (PLS)) is applied, with a dual – exploratory and predictive – purpose.

To maintain a truly loyal relationship and to achieve competitive success, hospitality managers must take into account both tangible and intangible features when allocating their marketing efforts to satisfaction-, trust- and commitment-based cues. On the other hand, the application of the PLS predict algorithm demonstrates the predictive performance (out-of-sample prediction) of our model that supports its ability to predict new and accurate values for individual cases when further samples are added.

LDA and PLS produce relevant informative summaries of corpora, and confirm and address more specifically the results of the previous literature concerning relationship quality. Our results are more reliable and accurate (providing insights not indicated in guests’ ratings into how hotels can improve their services) than prior statistical results based on limited sample data and on numerical satisfaction ratings alone.

This paper aims to explore the causal relationship of user-generated content (UGC) on trust in UGC (TUGC), destination image (DI) and tourist visit intention (VI) guided by the stimulus-organism-response (SOR) theory. This work further examined the mediating effect of TUGC and DI between UGC and VI.

Data were collected through online and personal-administered surveys and randomized sampling. This work employed partial least squares structural equation modeling (PLS-SEM) to test the proposed model empirically.

In line with the SOR Theory, this work found UGCs that induce positive emotions and connection to the users motivate VI rather than UGCs that are predominantly factual. Furthermore, UGCs are considered reliable, authentic and less biased than brand-generated content. The findings of this work contribute to the theoretical understanding of UGC to VI in a destination.

This work proposes that destination marketers prioritize UGC that evokes positive emotions and connections with users, as it is more effective in encouraging VI. Strategies such as incentivizing content creators, improving online presence and engaging influencers can maximize UGC. Enhancing online traffic quality, visibility and interaction and implementing content policies are crucial for UGC's effectiveness. Marketers should align destination products with tourists' interests and collaborate with influencers for affiliate marketing to increase tourist-generated UGC. Furthermore, improved connectivity encourages UGCs about the destination.

In tourism marketing, UGC has become a valuable information source for tourists in making informed travel decisions. UGC is a tourist-generated content that offers factual information and authentic experiences through images, videos or text posted through social media platforms. UGC is considered more reputable than travel firms and the mainstream media as an information source. Due to the limited works on UGC in the literature, the influence of UGC on tourists' VIs has remained unexplored at the time of writing. This work bridges this gap by empirically examining the impact of UGC on Gen Z tourists' VI guided by the SOR theory.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

The purpose of this paper is to explore maintenance strategies for manufacturing equipment in Chinese firms.

Data were collected from Chinese companies using a questionnaire administered during face‐to‐face interviews and two established methodologies in strategic management research, exploratory factor analysis and cluster analysis, were used to analyze the data.

The results suggest that despite increasing competitive capabilities of Chinese firms, their maintenance strategies are often restricted to corrective maintenance. Only very few Chinese firms have already implemented predictive maintenance approach, total productive maintenance programs or the strategic outsourcing of maintenance activities.

The research limitations stem from typical issues related to the use of exploratory factor analysis and cluster analysis (for example reliance on the subjective judgment of the researcher or the provision of clusters although no meaningful groups are embedded in the sample).

The findings highlight potential strategies for Chinese firms to improve their maintenance management.

This paper deals with a neglected area of operations management by exploring the maintenance approaches in fast growing Chinese manufacturing industries.

Considering the relevance of operational excellence as a business strategy, organizations are striving to improve themselves by adopting best practices and universally accepted principles through the process of continuous improvement, and these principles should be embedded in the culture of an organization. Organizations pursue to align themselves by continuously improving their processes by adopting scientifically proven techniques and cultural transformation throughout the organization. However, there is a lack of scientific instruments for the assessment of operational excellence. The objective of this study is to develop a scale for the assessment of practices of operational excellence principles in the organizations. Further reliability and validity of the developed scale are measured by testing the relationship between Human Resource Practices (HRP) and Operational Excellence (OE).

This study comprises quantitative design through exploratory and confirmatory studies and also includes qualitative analysis to develop a scale for the assessment of Operational Excellence (OE). Interviews from industry experts have been conducted to identify the major components for which organizations are striving for OE. Previous literature and excellence models, especially principles of the Shingo Operational Excellence Model (SOEM), have been reviewed and considered to finalize the scale items. Data were collected in two stages from both Telecommunication subsectors (Cellular Mobile Operators and Fixed Local Loop Operators) of Pakistan through the cross-sectional survey. In the first stage, exploratory factor analysis (EFA) was performed on the sample of 611 respondents from both Cellular Mobile and Fixed Local Loop operators of Pakistan. In the second stage, confirmatory factor analysis (CFA) was performed on the sample of 423 respondents from the Fixed local loop operators. EFA was conducted by using SPSS version 23 to finalize the OE scale, and for confirmatory factor analysis, PLS-SEM using Smart PLS was used to confirm the reliability and validity of the OE Scale.

The results of EFA reveal that OE is a multidimensional construct with three dimensions and 23 items. The dimensions of the developed OE Scale explored in this study are cultural enablers (CE), continuous process improvement (CPI) and enterprise alignment (EA). The confirmatory factor analysis of OE confirmed the scale dimensionality, reliability and validity along with the hypothesis testing to measure the impact of antecedent variable HRP on OE.

Organizations pursue to improve and align their operational processes but usually unable to confirm the implementation of their desired objectives. Based on the developed OE scale, managers may assess the implementation of OE principles in their organizations. This research has been conducted in the telecommunication sector of Pakistan only, and the developed instrument needs to be further tested in other organizations.

The instrument developed in this study will help both researchers and practitioners to assess the principles of operational excellence in their organizations and enable them to design the strategies for improving organizational performance.

The results of this study will create awareness about the principles of operational excellence. The developed OE instrument will assist in identifying the gaps in organizational norms and values from the perspective of paying respect to every individual inside and outside the organization. OE instrument will be further helpful in the identification and assurance of health, safety, protection of the environment and community issues.

This study provides a reliable and validated scale for the scientific area of operation management and helps managers with the assessment of operational excellence in their organizations. This newly developed scale is also valid to test and use in different studies and industries by researchers and practitioners.

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.

Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field’s dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field.

The authors searched six journals with an international business (and marketing) focus (Management International Review, Journal of International Business Studies, Journal of International Management, International Marketing Review, Journal of World Business, International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past.

Of the articles, 379 utilized CB-SEM and 45 PLS-SEM. The reasons for using PLS-SEM referred largely to sampling and data measurement issues and did not sufficiently build on the procedure’s benefits that stem from its design for predictive and exploratory purposes. Thus, the procedure’s key benefits, which might be fruitful for the theorizing process, are not being fully exploited. Furthermore, authors need to better follow best practices to truly advance theory building.

The authors examined a subset of journals in the field and did not include general management journals that publish international business and marketing-related studies. Fur-thermore, the authors found only limited use of PLS-SEM in the journals the authors considered relevant to the study.

The study contributes to the literature by providing researchers seeking to adopt SEM as an analytical method with practical guidelines for making better choices concerning an appropriate SEM approach. Furthermore, based on a systematic review of current practices in the international business and marketing literature, the authors identify critical challenges in the selection and use of SEM procedures and offer concrete recommendations for better practice.

The purpose of this research is to examine the construct and predictive ability of a German version of the Learning Transfer Systems Inventory (GLTSI), an instrument designed to assess a constellation of 16 factors known to influence the transfer of training in work settings.

The survey data for this study was collected from 569 employees working in 17 private sector organizations in Germany. This data was used to test the construct validity of the GLTSI using principle components analysis (common factor analysis). The predictive validity of the instrument was tested against individual and organizational level outcome measures using hierarchical stepwise multiple regression.

Exploratory factor analysis of the GLTSI produced a 16 factor solution that was a highly consistent original English version of the LTSI. The results of the regression analysis showed that a subset of GLTSI scales were significant predictors of measures of individual transfer results and organizational performance.

The study relies on self‐report data for both the independent and dependent variables and, although there is no evidence suggesting this was a problem, factors such as common method variance or social desirability could have influenced the correlations in this study in unknown ways. Certainly future criterion‐related validation research with the GLTSI using outcome measures that do not rely on self‐reports would be valuable. Future research with the GLTSI should also seek to modify certain items or add new items to enhance the emic or culture‐specific nature of specific scales.

This study contributes the study and practice of human resource development by furthering the dissemination of analytical tools across linguistic settings and enhancing the potential for the cross‐cultural study of learning transfer and training effectiveness.

The purpose of this study is to examine the (1) psychometric properties of Crossley and Highhouse's job search strategy scale and (2) the predictive utility of the scale on fit perceptions.

Data were collected from unemployed job seekers in Ghana (n_T1 = 720; n_T2 = 418). Exploratory and confirmatory factor analyses were conducted to examine the data.

Exploratory factor analysis on the first random sub-sample (n = 362) supported a three-factor model. Confirmatory factor analysis on the second random sub-sample (n = 358) confirmed the three-factor structure and was invariant across job search contexts and genders. Moreover, structural path results showed that the use of focussed and exploratory job search strategies facilitated positive fit perceptions and the use of haphazard job search resulted in poor job fit perceptions.

This study is the first to examine the dimensionality of job search strategies based on different job search context by linking it to fit perceptions. Moreover, the authors provide evidence that the job search strategy scale has a valid psychometric property and a promising instrument to assess job search behaviour across job search contexts and genders in an understudied population.

This paper aims to examine the impact of predictive gamification, through a sales simulator game, as a strategy to minimize sales problems in organizations.

This empirical study was executed in a large global company that produces and markets footwear for B2B and B2C markets. The company operates in more than 18 countries and adopts the gamification to train 7,600 sellers and 10,052 professionals directly linked to the sales area. The methodology adopted followed a qualitative and exploratory approach based on an in-depth case study analysis.

Results indicated that the use of predictive gamification allowed mitigating future problems, in average, four months in advance of the arrival of the new products at the points of sale. The main findings included improvement in sales arguments, product knowledge and sales closure. The company assigns the results to the implementation of the gamification and the possibility to simulate the evaluation of the level of readiness of the sales force resources. For this, the following three primary variables emerged: adequate definition of training content, analysis of the learning process and simulation of store reality.

The authors introduced to the sales training literature the concept of predictive gamification. The predictive gamification allows anticipating and reducing future problems before the arrival of the new products in the market, improving the argument of sales, the knowledge about the products and the ratio of sales conversion of stores.

First, the content design of gamification needs to be carefully projected before the simulator development, considering the target audience and its particularities. Second, the construction of evaluation activities needs to consider contexts, actions and results. Third, monitoring the paths taken by the seller in the simulator environment is requisite. Fourth, the analysis of the results of vendor responses in the simulator environment can be obtained through learning management system reports. Fifth, the development of game simulators with advanced technologies may be done with low investment.

The findings have implications for the field vis-à-vis three main research gaps identified in the literature discussing gamification in sales training. First, the authors identified best practices from the application of gamification in sales training. Second, they showed the sales qualification processes, which can be improved by applying gamification. Third, they presented strategies of use of gamification as an approach allied to the training of sales professionals to generate enhanced sales results.