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The purpose of this paper is to test the relationship of work-life culture and organizational productivity and determine if it is mediated by the availability of work-life programs.

Quantitative data for the study were collected using three sources: an original survey completed by managers of 195 different companies, archival data from two databases, and archival data published in three national surveys. Hypotheses were tested using path analyses.

The data reveals that work-life culture has no direct effect on labor productivity but does have an indirect effect on it, through the availability of work-life programs.

One of the study’s limitations is that its design is cross-sectional. The authors suggest that future longitudinal studies examine the impact of work-life culture on organizational outcomes.

Practitioners should note the importance of promoting a favorable work-life culture and offering work-life programs as they enhance labor productivity.

The authors examine the impact of work-life culture on organizational productivity, a relatively understudied relationship at the organizational level.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

Actualmente, Sol Meliá es la compañía líder en el mercado español por su oferta en hoteles urbanos, vacacíonales, y de reuniones y convenciones. Además de dominar la industria hotelera española, disfruta también de una posición de privilegio a nivel internacional. En el presente artículo se estudia su origen y su evolución durante las últimas cuatro décadas, periodo en el que ha pasado de disponer de un hotel en alquiler en Mallorca a convertirse en una compañía de servicios hoteleros que, a través de la gestión o franquicia, explota 227 establecimientos repartidos en 4 continentes y 25 países.

This research aims to answer two major research questions related to the COVID-19 crisis from a longitudinal approach: What is the revenue management (RM) role during the different periods subject to analysis? What are the RM strategies and measures implemented during this crisis in contrast with a non-crisis context? It also aims to propose an RM implementation model that provides a contingency plan to face future crises.

This qualitative study, following a longitudinal approach, analyses three round-table discussions with 11 internationally renowned experts during three key scenarios of the COVID-19 crisis: the lockdown period (from March to June 2020) and the following two summer seasons (the post-lockdown period): Post-lockdown I (the summer campaign, 2020) and Post-lockdown II (the summer campaign, 2021). Based on a deductive approach, thematic analysis is conducted using NVivo.

Further professionalisation of revenue managers, which has enabled the correct application of strategies and measures, highlighting the importance of not lowering prices, the flexibility of booking conditions, the development of other sources of income and the increase in the value of services, amongst others, are key factors in managing this crisis. The longitudinal analysis carried out in three different periods of this crisis shows how these measures have evolved and the contrast with RM application in a non-crisis context. The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RM systems (RMSs) by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.

This study has various limitations. First, the three round-table discussions were held online due to the health crisis, and the chosen webinar format may have biased the participants' answers due to its public nature. Second, the survey was carried out in Spanish. Despite the strong international profiles of the participants, cultural distortion may appear, suggesting that the research should possibly be extended to other cultural contexts in the future. Third, some of the participants were unable to attend all the round-table discussions due to their professional duties, so people with similar profiles were invited to the rest of the sessions.

The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RMSs by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.

This work contributes to the literature by providing a model that considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context. The novelty of this research is mainly found in the conducting of a deductive and longitudinal study considering previous research focussed on RM strategies applied during the COVID-19 crisis and supplementing it with new measures by applying qualitative techniques.

The quality of tourist accommodation establishments is a recurring theme in public strategies for planning and managing tourist destinations. The applying standards as a way to achieve quality. This strategy consists of legally regulating a series of minimum physical standards by using measureable parameters, as well as the desired characteristics or levels of amenities, which vary according to the type of provision required. The purpose of this study is to analyze the strengths and weaknesses of applying strategies that regulate standards for accommodations in coastal tourism areas in the Canary Islands (Spain).

The authors used an explanatory case study methodology. An “inter-case” comparison has been chosen, because the object of analysis (public policy applying tourism standards) has changed over time; furthermore, a content analysis approach was selected so that the subject of this research is the process of implementing this kind tourism policy. The result is a systematization of the process in stages.

Quantifiable criteria that exclusively consider the physical or operational parameters of a tourist establishment are no longer sufficient to provide quality service that meets the needs and expectations of customers. It is not enough to set standards for surface area parameters or essential amenities when organizing new tourist developments, but rather it is the qualitative aspect that must be addressed. At the same time, quality tourist accommodation establishment is not obtained merely by reducing density, which constitutes the significant standard.

This paper evidence is presented that may influence the quality of accommodation perceived by the client, which increases their level of satisfaction and, in turn, the degree of trust and, therefore, fidelity, understood as the future decision to repeat or not the tourist experience. There are also issues related to the approach that the quality of accommodation establishments has a direct effect on the visitor’s perception of the destination, which cannot be merely physical, mensurable in square metres.

The relationship between urban standards and quality is no longer linear. This determines that the regulated tourist accommodations (defined by sectoral regulations) it is common to set a required plot size in m2 based on the number of bed places offered by the establishment is no longer valid. Today, tourists rate accommodation establishments by the prestige of their brand or the diversity of services and experiences (entertainment, sensations, emotions, etc.) they provide. These experiences play such an important role in producing customer satisfaction and loyalty to a hotel that guests are willing to pay more for their stay, with the understanding that they will be able to experience certain emotions. It has been evidenced that the main reason for setting physical quality parameters that can be measured by a rating system for accommodation establishments, to provide a basic reference for customers, is no longer necessary, given the amount of easily accessible real-time information freely available through multiple independent channels based on the 2.0 paradigm, information technologies and communication, as well as applications and virtual platforms.

Applying this public strategy of classifying or rating tourist accommodations on a territorial scale has some strengths and weaknesses. However, it is not easy to find a comprehensive evaluation of the impact of such strategies because, among other issues, the public administration seems disinterested in measuring the consequences of their decisions, instead focussing on whether the formal legal requirements are being complied with. Moreover, whenever such assessments have been carried out, the discussion has been limited to the effectiveness of the implementation. This is why, beyond such purely mechanical responses, there are currently no studies or technical reports that specifically examine the positive or negative effects of such approaches. Evidently, these circumstances make any work analyzing this material relevant and timely. Likewise, the main reason for setting physical quality parameters that can be measured by a rating system for accommodation establishments, to provide a basic reference for customers, is no longer necessary, given the amount of easily accessible, real-time information freely available through multiple independent channels based on the 2.0 paradigm, information technologies and communication, as well as applications and virtual platforms.

The paper determines that the impact of the strategies of set a required plot size in square metres based on the number of bed places offered by the establishment is limited. Applying this public strategy of classifying or rating tourist accommodations on a territorial scale has some strengths and weaknesses. However, it is not easy to find a comprehensive evaluation of the impact of such strategies because, among other issues, the public administration seems disinterested in measuring the consequences of their decisions, instead focussing on whether the formal legal requirements are being complied with. Moreover, whenever such assessments have been carried out, the discussion has been limited to the effectiveness of the implementation. This is why, beyond such purely mechanical responses, there are currently no studies or technical reports that specifically examine the positive or negative effects of such approaches. Evidently, these circumstances make any work analyzing this material relevant and timely.

This paper aims to establish the main factors that underlie store attributes, to examine which exert the greatest influences on the achievement of a maximum level of customer satisfaction. This study seeks to determine if there are significant differences not only in the factor compositions but also in their influence on customer satisfaction, depending on the country of residence of focal customers.

The test of the proposed framework consists of analyses of two samples of customers that purchased in grocery stores in Spain and the USA. Following a factor analysis of the principal components, a binary logistic regression analysis tests the influence of the identified factors on customer satisfaction.

This work contributes to extant literature by assessing differences in the main factors that contribute to satisfaction with food stores, depending on the location of the customer.

This work is especially useful to grocery retailers that operate, or plan to operate, in different countries; it outlines key factors to consider to achieve upper‐bounded customer satisfaction scores.

The proposed classification of attributes and factors, according to their importance for customers' evaluations in different countries, includes three main factors. The first‐order factor includes the most valued attributes by all customers, independent of the country of residence. The second‐order factors include attributes with lesser importance though still valued by customers; the importance depends on the country of residence. Finally, the third‐order factor attributes are valued relatively less.

The purpose of this paper is to incorporate explicitly consumer heterogeneity into market response models estimated with store‐level scanner‐data.

Latent structures in market response to a product category using aggregated scanner data registered by a supermarket are identified. Specifically, latent consumer segments with diverse preferences towards brands and different responses to marketing stimuli from data consisting of daily marketing actions (i.e. price, promotions, advertising, etc.) and sales of competing brands are identified.

The existence of different latent segments with diverse preferences and response patterns to marketing stimuli were detected. More specifically, the fit of the statistical analysis for the different model possibilities made it possible to identify four market segments. It was also found that the intrinsic brand attractiveness as a measure of consumer brand preference is different between segments. Finally, the price sensitivity is also different between segments.

The time cost necessary to obtain the parameter estimates is too high, which is usual in the models estimated with iterative EM algorithms.

This work deepens one's knowledge of the identification and selection of latent market structures, specifically latent segments with different purchase patterns and behaviours. The possibility of developing the analysis with aggregated data at the store level increases the potential utility for academics and marketing managers.

Although most applications use weekly data, this proposal models daily fluctuations in sales – as a result, making it possible to obtain consumer segments based on daily changes.

To analyze the impact of temporary retail price discount on a consumer goods product category using semiparametric regression and considering different promotional price discount characteristics as well as brand characteristics.

A semiparametric regression model using Support Vector Machines, which aim to evaluate retailers' decisions about temporary price discounts, has been developed. The model is derived from the analysis of historical sales data, which provide precise evaluation of previous temporary price discounts periods. The model is also consistent with ample empirical evidence showing that historical retail sales data can be used to evaluate the impact of past promotions.

Provides an estimation of the shape of the deal effect curve, indicating which temporary price discounts are more effective to increase sales and showing the existence of different threshold and saturation levels. Confirms that promotional price discounts accelerate sales especially during week ends. Evidences that promoting high‐priced (high‐quality) brands has a stronger impact on sales of low‐priced (low‐quality) brands than the reverse and that cross‐price effects are stronger on the sales of brands with similar prices. Suggests the convenience of the use of the proposed semiparametric methodology to the study of the promotional effects considered.

It is not possible to generalize the modelled shapes of the deal effect curves. There is no information available on feature advertising nor displays. It is important to determine the generalizability of these results to the study of additional promotional effects. It would also be interesting to assume that the retailer's deal policy is exogenous.

Provides a relevant tool to assess the set of price promotional periods by the grocery retailer. With a more precise and accurate knowledge about the performance of past temporary price cuts, retailers can implement more effective promotional periods.

The curricular profile that businesses demand from their workers is undergoing considerable change, especially in regard to university graduates. Considering the functions that are associated with universities, they should respond to these business demands. As a result, they should educate their students following curricula that fit the needs of the labour market. This fit would allow improving the employability of university graduates. The objectives of this work consist of determining the competencies that firms demand from university graduates and determining the curricula that universities follow to educate their students.

Through a comparison of these objectives, the paper analyses the mismatches that exist between these competencies and the curricula, and action lines are proposed to correct them.

The results show the various competencies that universities must improve upon in the education of their students. This improvement would mean a better fit between university curricula and business demands.

The research was applied in one region of Spain, and it would be appropriate to expand it to the entire country.

University managers can use the information obtained from this study as the basis for designing and subsequently implementing action lines that allow improving the curricular profile of university graduates.

There are few papers that analyse the competencies that firms demand from university graduates and that determine the curricula that universities follow to educate their students.

The aim of this study is to explore and understand corporate governance patterns in family firms across Latin America. This is in response to several calls in the academic literature urging for more empirical studies in corporate governance in developing regions.

Following a configurative perspective, a hierarchical cluster analysis is applied to a sample of the 155 largest Latin American family firms.

The authors identify three main corporate governance configurations across Latin American countries. First, the exported governance model resembles many characteristics of Anglo-American and Continental Europe governance patterns of public listed control, having independence from the board of directors, and mainly hiring non-family management. Second, the super-familial governance model describes private ownership where one or multiple families control both the board of directors and the top-management team. Finally, the hybrid governance model is the largest cluster identified in the sample and combines governance characteristics of both of the foregoing configurations. This configuration exhibits ownership structured through public offerings of shares combined with leadership of the board of directors by a family member as well as moderate family influence on the board and management.

This is the first study to investigate corporate governance in the largest listed and privately-owned family firms in Latin America. The article extends the conversation on family firm heterogeneity and contributes to the configurative approach in the family business field by offering a cross-country perspective and identifying meaningful taxonomies that are applicable beyond national boundaries.