Search results

Self-service check-out technologies (SSTs) are becoming a trend across different retail settings, allowing companies to gain efficiency and reduce costs. Nevertheless, the success of SSTs implementation is still subject to challenges and uncertainties. This study aims to provide insights for theory and managers on the necessary conditions for the successful implementation of retail SSTs.

Based on an online survey, data from 251 participants were collected to understand the factors predicting SSTs adoption and realise what conditions are mandatory for the adoption. partial Least Squares Structural Equation Modelling (PLS-SEM) and necessary condition analysis (NCA) were used to analyse the data.

According to the NCA analysis results, 12 latent variables were relevant for predicting SSTs adoption, but only seven were necessary conditions for user adoption.

The complementarity of perspectives for understanding the adoption of SSTs based on the two data analysis techniques provides novel insights into theory and support for retailers' decision-making on self-service technologies (STTs) implementation.

This study focuses on memorable customer shopping experience design in the sporting goods retail setting. It aims to identify the phygital customers' needs and expectations that are satisfied through in-store technologies and to detect the in-store strategies that use these technologies to make the store attractive and experiential.

This exploratory study adopted a qualitative research methodology, specifically a multiple-case study, by performing semi-structured interviews with sporting goods store managers.

Sporting goods retailers use various in-store technologies to create a phygital customer shopping experience, including devices, mobile apps, wireless communication technologies, in-store activations, support devices, intelligent stations, and sensors. To improve the phygital customer journey and the phygital shopping experience, retailers meet customers' needs for utilitarian, hedonic, social, and playfulness experiences. Purely physical or digital strategies, as well as phygital strategies, are identified. This research also proposes a model of in-store phygital customer shopping experience design for sporting goods retailers.

Sporting goods managers can invest in multiple technologies by designing a physical environment according to the customers' needs for utilitarian, hedonic, social, and playful experiences. In addition, they can improve the phygital customer shopping experience with specific push strategies that increase customer engagement and, in turn, brand and store loyalty.

This study highlights how the phygital customer experiential journey can be created through new technologies and improved with specific reference to the sporting goods stores.

The Covid-19 pandemic has a strong effect on societies, business and consumers. Governments have taken measures to reduce the spread of the pandemic, such as social distancing and lockdowns. The latter has also resulted in a temporary closure of physical stores for “non-essential” retailing. Covid-19 thus has a profound impact on how people live. The period of relative isolation, social distancing and economic uncertainty changes the way we behave. New consumer behaviors span all areas of life, from how we work to how we shop to how we entertain ourselves. These shifts have important implications for retailers. This paper aims to discuss the potential structural effect on shopping behavior and retailing when Covid-19 measures are no longer needed and society moves back to a normal situation.

The paper synthesizes empirical and conceptual literature on the consequences of COVID-19 and introduces a conceptual framework along with a set of predictions that can be investigated with empirical data.

This study suggests that Covid-19 shapes both consumer needs and behavior and how retailers respond to these changes. Moreover, it suggests that this will not only affect market outcomes (i.e. retail sales and market share online) but also firm outcomes (i.e. customer experience, firm sales) and importantly the competition between online and offline retailers.

In the conceptual framework, this study aims to advance knowledge on longer-term outcomes (vs immediate outcomes such as panic buying) and how COVID-19 is changing the competitive landscape of retail.

This study aims to, first, propose a valid and reliable scale to document the COVID-19 Pandemic Shopping Experience (CPSE) and, second, determine the impact of its variables on the postpurchase shopping experience (PPSE).

For scale development, published studies were scanned and the variables were shortlisted. These shortlisted variables were validated by 52 faculties from four universities in Saudi Arabia. Data were collected from 318 respondents to purify the CPSE Scale. In Study 2, a path analysis was performed on a sample of 354 respondents to determine the individual impact of each variable on PPSE.

A total of 14 items were found to be aligned under four variables, social distance (SD), shop hygiene, operational time and entertainment venues. SD was found to have the greatest influence on PPSE, followed by operational time and shop hygiene.

This research has important implications for retailers to initiate changes in store layout so that they can implement social distancing by physically marking stickers on the floors and by placing barricading on billing counters. Store hygiene can be ensured by making sanitizers and hand gloves available at the entry points, periodically cleaning the floor and sanitizing the premises. Rationing the operating time proved to be an effective tool to minimize the exposure time, thereby limiting consumers' time inside the store.

To the best of the authors’ knowledge, this is the first study to propose a full-scale measure of the customer shopping experience (SE) during a pandemic. This scale can be generalized to measure SE in similar situations.

The purpose of this paper is to provide an alternative framework that will assist in understanding the adoption of digital food shopping. The coronavirus disease 2019 (COVID-19) pandemic has exacerbated the demand for digital shopping, but the adoption of digital shopping for food has not accelerated as fast as in other product categories. This study considered the role of socio-cultural factors to understand the reason for slow adoption of digital technology to access food. A cultural framework that can be used to investigate socio-cultural factors in this context was lacking, however, this paper provides a discussion of social and cultural factors and developed measurement scales to assist in understanding cultural change acceptance in consumers' adoption of digital technology to purchase food.

Using Hayes' process analysis, this paper investigated how cultural acceptance – mediated by consumer affection and appeal and measuring the moderated effects of digital trust (DT) – determined the eventual impact on consumer intention to adopt digital food retailing. This paper also considered moderated mediation with parallel mediations (consumer affection and appeal, digital convenience (DC) and consumer digital readiness) interacting with DT and consumer learning.

The authors found that cultural acceptance of digital technology (CADT) is an antecedent to the adoption of digital shopping for food, but this is also mediated by consumers' appeal and affection for digital technology and consumers' digital readiness.

This study also indicates that DT influences consumer appeal and affection (CAA), especially amongst female consumers.

The paper represents an empirical investigation of a new conceptual framework that considers socio-cultural factors to understand consumers' use of digital technology in food shopping which has been an existing knowledge gap in current literature.

Service robots can offer benefits to consumers (e.g. convenience, flexibility, availability, efficiency) and service providers (e.g. cost savings), but a lack of trust hinders consumer adoption. To enhance trust, firms add human-like features to robots; yet, anthropomorphism theory is ambiguous about their appropriate implementation. This study therefore aims to investigate what is more effective for fostering trust: appearance features that are more human-like or social functioning features that are more human-like.

In an experimental field study, a humanoid service robot displayed gaze cues in the form of changing eye colour in one condition and static eye colour in the other. Thus, the robot was more human-like in its social functioning in one condition (displaying gaze cues, but not in the way that humans do) and more human-like in its appearance in the other (static eye colour, but no gaze cues). Self-reported data from 114 participants revealing their perceptions of trust, anthropomorphism, interaction comfort, enjoyment and intention to use were analysed using partial least squares path modelling.

Interaction comfort moderates the effect of gaze cues on anthropomorphism, insofar as gaze cues increase anthropomorphism when comfort is low and decrease it when comfort is high. Anthropomorphism drives trust, intention to use and enjoyment.

To extend human–robot interaction literature, the findings provide novel theoretical understanding of anthropomorphism directed towards humanoid robots.

By investigating which features influence trust, this study gives managers insights into reasons for selecting or optimizing humanoid robots for service interactions.

This study examines the difference between appearance and social functioning features as drivers of anthropomorphism and trust, which can benefit research on self-service technology adoption.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Digitalisation is perceived as a new process that may add value to firms. Current theoretical understanding assumes it should be part of a firm's strategy to respond to multiple pressures in the business environment. This paper explores the occurrence of digitalisation in a rare context, that of the English agricultural industry in the United Kingdom, a place disproportionality filled with family firms. The general understanding of digitalisation in family firm settings remains embryonic. The authors' explorations make theoretical contributions to research at the intersection of rural entrepreneurship, family business and innovation.

Utilising a purposive, qualitative approach, primary data was collected from multiple interviews with 28 UK family farms, and secondary data from another 164. Interview transcripts were coded using NVivo, along with secondary data from reports, observations and websites.

The authors present empirical evidence illustrating how digitalisation manifests incrementally and radically in different types of family farms. The authors present a model that shows the areas of farming that have, and continue to be, digitalised. This increases analytical precision when identifying digitalisation activities that differ depending on the strategy to either scale or diversify. The authors propose that incremental digitalising occurs to a great extent during a scaling strategy, and that radical digitalising occurs to a smaller extent during diversification strategies in family farms.

This research uses a sample of family-run farms from the UK agricultural sector to explore nuanced elements of digitalisation. It should therefore be explored in other types of family firms located in different sectors and geographies.

This research is important because family farms are under increasing pressure and have limited financial resources to deal with the digitalisation agenda. Therefore, empirical evidence helps other farms in similar situations. The authors found digitalisation investments, that tend to be capital intensive, only matter for scalers and less so for diversifiers. Family farms can use the model presented as a tool to evaluate their farm. The tool helps them define what to do, and ideate the potential activities that might be digitalised, to feed into their wider strategy.

Family firms, in particular farms, are critical to many economies. The general consenses currently assumes all family firms should digitalise, yet the authors' evidence suggests that this is not the case. It is important to create policies that are sensitive to the needs of different types of businesses, in this case between family firm scalers and diversifiers, instead of simply incentivising digitalisation using a blanket approach usually by offering financial aid. Understanding how digitisation can support (or not) family firm resilience and growth in an effective and efficient manner can have significant benefit to individual firms, and across industries.

The proposed model extends theoretical understanding linking strategy, digitalisation activity and innovation in family farms. It shows that digitalisation is a key building block of scaling strategies, maximising digitalisation to increase efficiency. Yet, diversifying family farms minimise digitalisation, whereby they only digitalise a small amount of the farming activity. This empirical evidence contrasts with the wider narrative that farmers are slower at using new technology. This research found that some are slower because it does not align with their strategy. However, sometimes digitalisation aligns with their strategy during external changes, in which case the diversifiers are quick to act.

Long sales periods (i.e. Christmas time, Black Friday, etc.) and the risk of contagion while shopping (i.e. during pandemics like COVID-19) showed the extent to which crowded shopping environments have negative effects on consumers’ behavior. Hence, using terror management theory (TMT) and stimulus–organism–response (S–O–R) approach, the authors aims to develop a new model to investigate the effect of crowd-checking technology on shopping anxiety, the perceived value of shopping experience and willingness to pay.

Using a cross-sectional research design and with a help of an independent research company, the authors collected 1,000 valid responses from individuals residing in the UK.

Results showed that crowd-checking technology cues reduce shopping anxiety, improve value perceptions and increase willingness to pay. In addition, the efficacy of technology in reducing shopping anxiety is moderated by customers’ shopping orientation.

To the best of the authors’ knowledge, this research is the first one investigating crowd monitoring systems used at the initial stage of consumers’ journey to show the number of consumers in-store in real-time. It contributes to the literature on technology applications in retailing by providing evidence that technology cues reduce shopping anxiety, which in turn enhances customer-perceived value from the shopping experience and increases the willingness to pay in stores with embedded crowd-checker technology. The study also extends the application of TMT in a new context.

Los largos periodos de rebajas (ej. Navidades, Black Friday, etc.) y el riesgo de contagio durante las compras (ej. durante pandemias como la COVID-19) mostraron hasta qué punto los entornos de compras abarrotados tienen efectos negativos en el comportamiento de los consumidores. Utilizando la Teoría de la Gestión del Terror (TMT) y el enfoque Estímulo-Organismo-Respuesta (S-O-R), desarrollamos un nuevo modelo para investigar el efecto de la tecnología de control de multitudes sobre la ansiedad en las compras, el valor percibido de la experiencia de compra y la disposición a pagar.

Utilizando un diseño de investigación transversal y con la ayuda de una empresa de investigación independiente, recogimos 1.000 respuestas válidas de personas residentes en el Reino Unido.

Los resultados mostraron que las señales de la tecnología de comprobación de multitudes reducen la ansiedad en las compras, mejoran la percepción del valor y aumentan la disposición a pagar. Además, la eficacia de la tecnología en la reducción de la ansiedad de compra está moderada por la orientación de compra de los clientes.

Esta investigación es la primera que estudia los sistemas de seguimiento de multitudes utilizados en la fase inicial del viaje de los consumidores para mostrar el número de consumidores en la tienda en tiempo real. Contribuye a la literatura sobre aplicaciones tecnológicas en el comercio minorista aportando pruebas de que las señales tecnológicas reducen la ansiedad de compra, lo que a su vez mejora el valor percibido por el cliente de la experiencia de compra y aumenta la disposición a pagar en tiendas con tecnología de control de multitudes integrada. El estudio también amplía la aplicación de la Teoría de la Gestión del Terror en un nuevo contexto.

漫长的销售期（即圣诞节时间、黑色星期五等）和购物时的传染风险（即在COVID-19等大流行病期间）显示了拥挤的购物环境对消费者行为的负面影响程度。 因此, 利用恐怖管理理论（TMT）和刺激-组织-反应（S-O-R）方法, 我们建立了一个新的模型来研究人群检查技术对购物焦虑、购物体验的感知价值和支付意愿的影响。

在一家独立研究公司的帮助下, 我们采用横断面研究设计, 从居住在英国的个人中收集了1000份有效的答复。

结果显示, 人群检查技术提示减少了购物焦虑, 改善了价值认知, 并提高了支付意愿。此外, 技术在减少购物焦虑方面的功效受到顾客购物取向的调节。

这项研究是第一个调查在消费者旅程的初始阶段使用的人群监测系统, 以实时显示店内消费者的数量。它为零售业技术应用的文献做出了贡献, 提供了技术线索减少购物焦虑的证据, 这反过来又提高了顾客从购物体验中感知到的价值, 并增加了在有嵌入式人群检查器技术的商店中的支付意愿。该研究还扩展了恐怖管理理论在新环境中的应用。

This research aims to propose the development of a model that identifies, in essential services, the determining factors affecting the technological advances offered by different smart technologies in supermarket retail channels that influence citizens' quality of life, amidst the coronavirus disease 2019 (COVID-19) pandemic.

The data were collected using a cross-sectional questionnaire survey (n = 469). The authors applied the structural equation modeling (SEM) technique to test the hypotheses, along with the partial least squares (PLS) method for estimating latent variables and combining with the necessary condition analysis (NCA) method.

According to the results of the NCA method, the results were adequate, and more attention should be paid to the quality of life construct after finding the bottleneck point of 50%. In this sense, adaptive resilience was characterized as the main necessary predictor construct for quality of life. In addition, Generation Z and Millennials have the highest frequency of use in all smart technologies, with “assisted purchase” being the most widely used.

Finally, the effect of the pandemic changed the consumption routine with supermarkets, not being a mere option but a necessity in the context of a smart city.

As a result, the proposed model was consistent, showing that all direct and indirect SEM paths were validated, highlighting data security and privacy and resilience issues. In addition, the NCA method complemented the procedures performed in the SEM phase.