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This article examines the mechanisms through which big data analytics capabilities (BDAC) contribute to creating sustainable value and analyzes the mediating roles that supply chain management capabilities (SCMC), as well as circular economy practices (CEP), play through their impact on sustainable performance.

Following a literature review, a serial mediation model is presented. Hypotheses regarding direct and mediating relationships are tested to determine their potential for sustainability impact and circularity. Partial least squares structural equation modeling (PLS-SEM) has been applied for causal and predictive purposes.

The results indicate that big data analytics capabilities do not have a direct positive impact on sustainable performance but influence indirectly through SCMC and CEP.

Although some authors have addressed the associations between IT business value, supply chain (SC), and sustainability, this paper provides empirical evidence related to these relationships. Additionally, this study performs novel predictive analyses.

The purpose of this paper is to analyze the changes in wealth and consumption inequality in Spain and estimate the consumption effects of housing and financial wealth.

The estimations are made using micro-data from the Spanish Survey of Household Finances (2002–2014) applying cross-section, panel and interquartile techniques.

The findings of this paper suggest that there was an increase in wealth inequality during the period under analysis and a reduction in consumption inequality. Also, the authors find a significant positive effect of wealth on consumer expenditure. Disaggregating by asset type, the value of the main residence is the category with the highest estimated effect on consumption, whereas the remaining types of assets, although still positive and generally significant, have more modest effects on consumption. However, the estimated coefficients and their significance can change substantially depending on the phase of the economic cycle and the position of the household in the income distribution.

These results provide new empirical evidence on the effects of household wealth changes on their consumption behavior, the differences depending on the household's position in the distribution and the fluctuations of these estimated coefficients throughout a period of profound economic upheavals.

The purpose of this study is to estimate the housing wealth effect on non-durable consumption using data from the Spanish Survey of Household Finances (Encuesta Financiera de las Familias, SHF) for the period 2002–2017.

The authors aim at identifying the effect of anticipated and unanticipated housing wealth changes on consumption with the sample of homeowners, following Paiella and Pistaferri (2017).

Results of this study lead us to conclude that there exists a strong housing wealth effect on consumption for the Spanish households.

The authors provide evidence against the permanent income model. They also analyse how the results change with income expectations, age and the household indebtedness rate. Finally, they detect a strong excess sensitivity to income.

This paper aims to study the value of informal care (IC) time from the perspective of caregivers using two alternative contingent valuation tools – willingness to pay (WTP) and willingness to accept (WTA) – and to identify the variables that affect the stated values.

The authors used data from a multi-centre study of 610 adult caregivers conducted in two Spanish regions in 2013. The existence of “protest zeros” and “economic zeros” because of the severe budgetary constraints of the households was also considered. Two-part multivariate models were used to analyse the main factors that explained the declared values of WTA and WTP.

The average WTP and WTA were €3.12 and €5.98 per hour of care, respectively (€3.2 and €6.3 when estimated values for “protest zeros” and “economic zeros” were considered). Some explanatory variables of WTA and WTP are coincident (place of residence and intensity of care time), whereas other variables only help to explain WTP values (household and negative coping with caregiving) or WTA values (age and burden of care). Some nuances are also identified when comparing the results obtained without protest and economic zeros with the estimated values of these special zeros.

Studies analysing the determinants of WTP and WTA in IC settings are very scarce. This paper seeks to provide information to fill this gap. The results indicate that the variables that explain the value of IC from one perspective may differ from the variables that explain it from an alternative perspective. Given the relevance of contextual factors, studies on the topic should be expanded, and care should be taken with the extrapolation of results across countries and settings.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated.

DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a “benchmarking” target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the “furthest” efficient projection to the evaluated DMU. The projected point on the efficient frontier obtained as such may not be a representative projection for the judged unit, and consequently, some authors in the literature have suggested determining closest targets instead. The general argument behind this idea is that closer targets suggest directions of enhancement for the inputs and outputs of the inefficient units that may lead them to the efficiency with less effort. Indeed, authors like Aparicio et al. (2007) have shown, in an application on airlines, that it is possible to find substantial differences between the targets provided by applying the criterion used by the traditional DEA models, and those obtained when the criterion of closeness is utilized for determining projection points on the efficient frontier. The determination of closest targets is connected to the calculation of the least distance from the evaluated unit to the efficient frontier of the reference technology. In fact, the former is usually computed through solving mathematical programming models associated with minimizing some type of distance (e.g. Euclidean). In this particular respect, the main contribution in the literature is the paper by Briec (1998) on Hölder distance functions, where formally technical inefficiency to the “weakly” efficient frontier is defined through mathematical distances.

All the interesting features of the determination of closest targets from a benchmarking point of view have generated, in recent times, the increasing interest of researchers in the calculation of the least distance to evaluate technical inefficiency (Aparicio et al., 2014a). So, in this paper, we present a general classification of published contributions, mainly from a methodological perspective, and additionally, we indicate avenues for further research on this topic. The approaches that we cite in this paper differ in the way that the idea of similarity is made operative. Similarity is, in this sense, implemented as the closeness between the values of the inputs and/or outputs of the assessed units and those of the obtained projections on the frontier of the reference production possibility set. Similarity may be measured through multiple distances and efficiency measures. In turn, the aim is to globally minimize DEA model slacks to determine the closest efficient targets. However, as we will show later in the text, minimizing a mathematical distance in DEA is not an easy task, as it is equivalent to minimizing the distance to the complement of a polyhedral set, which is not a convex set. This complexity will justify the existence of different alternatives for solving these types of models.

As we are aware, this is the first survey in this topic.

The purpose of this study is to explore the paranormal tourist experience on ghost tours, taking into account the participants’ perceptions and their prior knowledge of the paranormal, in order to determine the principal components of the future of paranormal tourist experience.

The study is divided into two phases. The first phase is based on participant observation during a ghost tour. The second phase is based on a previously published framework for paranormal tourism. It introduces a qualitative adaptation of the orchestra model to look in-depth at how experiences with paranormal tours might shape the future of tourism as a major subtype.

Paranormal tourism has external (situational-enchantment, historical, mystical, ghostly and unsolved mysteries) and internal (affective, cognitive, sensory, behavior and relationship) components that are inter-linked. Future paranormal tourist experiences (FPTEs) must be focused on enhancing these aspects in order to offer an immersive experience.

Consequently, this paper proposes the FPTE model.

The authors analyze the relationship between different consumer attitudinal variables and a number of variables related to consumer perception of the store and purchasing behavior, in assortments composed exclusively of private labels (PLs).

The authors developed an experiment based on an online survey to test the hypotheses formulated. The model’s causal relationships are established using structural equations.

The image of stores that only offer their own brand is mainly configured by price consciousness and the attitude toward the private label. The private label purchase intention is strongly influenced by the store image and a favorable attitude toward the brand, and loyalty strategies should be aimed at securing a clear perception of providing real value.

For retailers who only offer their own brands, an assortment with price-competitive PLs is key to the strategy of differentiating them from other retailers. It is reasonable to assume that, if retailers have a favorable image, customers transfer this brand value to their PLs and trust them. Customer loyalty strategies of these retailers should be aimed at ensuring that consumers clearly perceive that their assortment provides real value and that, although it is limited in terms of number of brands, it can meet all their needs.

This research represents a significant contribution to brand management literature because, includes, together with loyalty to the store, its image and the PL purchase intention as consumer response variables. Another differentiating feature is the methodology used. Estimation of the structural equation model permits the simultaneous estimation of the relationships between the variables.

Analizamos la relación entre diferentes variables actitudinales de los consumidores y un número de variables relativas a la percepción de los consumidores con respecto al establecimiento y el comportamiento de compra, todo ello en surtidos compuestos exclusivamente por marcas de distribuidor.

Desarrollamos un experimento online, basado en una encuesta, para testar las hipótesis planteadas. Utilizamos ecuaciones estructurales para determiner las relaciones causales del modelo.

La imagen de los establecimientos que ofrecen exclusivamente su propia marca se configura, principalmente, por la conciencia de precio y por la actitud de los consumidores hacia la marca privada. La intención de compra de la marca de distribuidor está fuertemente influenciada por la imagen del establecimiento y por una actitud favorable hacia dicha marca, por lo que las estrategias de fidelización de clientes deberían estar orientadas a asegurar una clara percepción de proporcionar valor real a los consumidores.

Para los minoristas que ofertan exclusivamente sus propias marcas, un surtido con marcas de distribuidor muy competitivas en precio es fundamental en su estrategia de diferenciación de sus competidores. Además, es razonable suponer que si los minoristas cuentan con una imagen favorable, los consumidores trasladarán este valor de marca a sus propias marcas propias y confiarán en ellas. Las estrategias de fidelización de este tipo de minoristas deberían ir enfocadas a asegurarse de que los consumidores perciben claramente el valor real que aporta su surtido y que, aunque limitado en términos de número de marcas y alternativas, les permite cubrir todas sus necesidades.

Esta investigación supone una significativa contribución a la literatura sobre gestión de marcas al incluir, conjuntamente con la lealtad al establecimiento, su imagen y la intención de compra de la marca de distribuidor como variables respuesta del consumidor. Otro elemento diferenciador es la metodología empleada, ya que la estimación del modelo de ecuaciones estructurales permite la estimación simultánea de las relaciones entre las distintas variables.

The purpose of the paper is to present a service design (SD)-based methodology developed to help small and medium enterprises (SMEs) undertake organisational change.

This research used the design science research methodology, which enabled the creation of the Service Design for Organisational Change (SD4OCh) methodology. A real case study of a small service company specialised in neuropsychological disorders was used for the definition and validation of SD4OCh.

The main outcome of this study is the SD4OCh methodology, which is based on three key stages: diagnosis (knowing where to begin by detecting the organisation's strengths and weaknesses), innovation (improving the structure/processes and designing/redesigning services by employing a customer-centric approach), and implementation (enabling the definition of the route towards organisational change). There is also a transversal evaluation stage, which quantifies the organisational changes.

This study adds valuable knowledge to the service science research field and contributes to the awareness of the usefulness of SD theory within companies, especially those which are small and medium-sized, since those companies lack the tools and methods required to tackle organisational change, signifying that the challenges the companies confront are different to those of larger companies.

Although this is a SD-based research, the SD4OCh methodology was developed in order to enable companies to make holistic changes, namely, to innovate their services, structure, and processes, thus supporting and guiding organisational change.