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This study aims to identify typologies of women who take holiday decisions within the couple, characterizing their profile through their lifestyles and some socio‐demographic variables.

The information was obtained through a survey addressed to Spanish women who were married or lived with their partner. Data were used to perform two types of analyses: scales validation and cluster analysis.

Three different groups have been found depending on the importance the women give to pre‐ and during‐holiday decisions. These groups present specific socio‐demographic characteristics, interests and opinions. However, findings reveal the need for further research into women's lifestyles as an explanatory variable.

This information will contribute not only to the academic knowledge, but will also help tourist managers to create competitive offers. It will also allow managers to implement more efficient promotional campaigns with the aim of attracting female tourists.

A classification of women, based on their holiday decisions, their lifestyles (activities, interest and opinions, AIO scale) and on some socio‐demographic variables (age, time living with the partner, children, occupation, level of studies, personal and family income, etc.), is provided in order to characterise them.

The purpose of this paper is to demonstrate that human resource management (HRM) has an impact on supply chain management (SCM), which in turn has a significant impact on customer satisfaction (CS) and organisational performance (OP), playing a mediating role in the relationship between HRM and SCM outcomes (SCMO).

The model is first validated and the hypotheses formulated are tested using the partial least squares structural equation model (SEM), based on five constructs: HRM, SCM implementation (SCMI), SCMO, customer satisfaction and organisational performance, taken from an existing model tested previously in a different geographical context. To do this, a survey was conducted and 231 valid responses were obtained.

The empirical results reveal that HRM had significant direct and indirect impacts on SCMO, and SCMI, which in turn played a mediating role in the relationships between HRM and SCMO. CS also played a mediating role in the relationships between SCMO and OP. This finding suggests that the successful implementation of SCM not only directly improves SCMO, but it also indirectly increases CS and OP.

A firm’s human resource practices need to be aligned with its SCM to foster the involvement of the members of the supply chain (SC), promote the integration of the SC and, consequently, ensure better business outcomes.

The study provides an original analysis not only in terms of the measurement of the relationship between HRM and SCM, but also with regard to its examination of the mediating effects, thus shedding light on the mechanisms by which these relationships are produced, and how this impacts CS and OP. This has allowed us to obtain more insightful results than those reported in the literature to date.

Reviews the latest management developments across the globe and pinpoints practical implications from cutting‐edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The growing awareness worldwide of the need for us all to adopt sustainable behaviors has had, and continues to have, massive repercussions for all aspects of the way we live our lives. However, business organizations with genuine environmental credentials can achieve differentiation and competitive advantage.

Provides strategic insights and practical thinking that have influenced some of the world's leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy‐to digest format.

Reviews the latest management developments across the globe and pinpoints practical implications from cutting‐edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Fortunately, consumers are getting the message that the environment matters, our resources are finite, and how organizations go about their work is and should be a matter of intense public scrutiny.

Provides strategic insights and practical thinking that have influenced some of the world's leading organizations.

Provides strategic insights and practical thinking that can have a broader social impact.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy‐to digest format.

Based on the human resource (HR) and supply chain integration (SCI) literature, this paper aims to argue that high-involvement human resource practices (HIHRP) work as a complementary capability for SCI, and thus, HIHRP moderates the relationship between SCI and firm productivity. This moderating role is analyzed through the following HIHRP dimensions, namely, ability-enhancing, motivation-enhancing and opportunity-enhancing practices framework.

Using empirical data collected from a survey of the agri-food sector (horticultural firms of southern Spain), the moderating effects of HIHRP on the relationship between supply chain external integrations (with customers and suppliers) and productivity are examined. Hierarchical regression analysis is used to test the proposed hypotheses.

The results support that HIHRP has a moderating effect on the SCI/productivity relationship. However, these results are only significant in the case of supplier integration.

This study analyzes HIHRP as a complementary asset in the context of SCI and makes both theoretical and managerial contributions to the SCI literature by empirically analyzing the role of HR practices in enhancing the relationship between SCI and performance.

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.