Past, present, and future research on self-service merchandising: A co-word and text mining approach

Purpose This study aims to discern emerging trends and provide a longitudinal perspective on merchandising research by identifying relationships between merchandising-related subdomains/themes. Design/methodology/approach This study sourced 657 merchandising-related articles published since 1960, from the Scopus database and 425 from Web of Science. After processing and normalizing the data, this study performed co-word and thematic network analyses. Taking a text mining approach, this study used topic modeling to identify a set of coherent topics characterized by the keywords of the articles. Findings This study identified the following merchandising-related themes: branding, retail, consumer, behavior, modeling, textile and clothing industry and visual merchandising. Although visual merchandising was the first type of merchandising to be used in-store, only recently has it become an emerging topic in the academic literature. There has been a further trend over the past decade to understand the adoption of simulation technology, such as computer-aided design, particularly in supply chain management in the clothing industry. These and other findings contribute to the discussion of the merchandising concept, approached from an evolutionary perspective. Research limitations/implications The conclusions of this study hold implications at the intersection of merchandising, sectors, new technologies, research methodologies and merchandising-practitioner education. Research trends suggest that, in the future, virtual reality and augmented reality using neuroscientific methods will be applied to the e-merchandising context. Practical implications The different dimensions of merchandising can be used to leverage store managers’ decision-making process toward an integrated store-management strategy. In particular, by adopting loyalty merchandising tactics, the store can generate emotional attachment among consumers, who will perceive its value and services as unique, thanks to merchandising items designed specifically with that aim in mind. The stimulation of unplanned purchases, the strategic location of products and duration of each merchandising activity in the store, the digitalization of merchandising and the application of findings from neuroscience studies are some of the most relevant practical applications. Originality/value This study provides the first-ever longitudinal review of the state of the art in merchandising research, taking a holistic perspective of this field of knowledge spanning a 60-year period. The work makes a valuable contribution to the development of the marketing discipline.info:eu-repo/semantics/acceptedVersio

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications

Digital payments adoption research: A meta-analysis for generalising the effects of attitude, cost, innovativeness, mobility and price value on behavioural intention

yesThe rapid evolution of mobile-based technologies and applications has led to the development of several different forms of digital payment methods (DPMs) but with limited enthusiasm in consumers for adopting them. Hence, several academic studies have already been conducted to examine the role of various antecedents that determines consumers’ intention to adopt DPMs. The degree of effect and significance of several antecedents found to be inconsistent across different studies. This provided us a basis for undertaking a meta-analysis of existing research for estimating the cumulative effect of such antecedents. Therefore, this study aims to perform a meta-analysis of five antecedents (i.e. attitude, cost, mobility, price value and innovativeness) for confirming their overall influence on intentions to adopt DPMs. The results of this study suggest that the cumulative effect of four out of five antecedents found to be significant while influence of price value was found insignificant on behavioural intentions. The recommendations drawn from this research would help to decide if and when to use such antecedents for predicting consumer intention to adopt DPMs

Perpetrating Cyber Dating Abuse: A Brief Report on the Role of Aggression, Romantic Jealousy and Gender

There is increasing evidence that the use of elec-tronic communication technology (ECT) is being integrated into romantic relationships, which can be used as a medium to control a romantic partner. Most research focuses on the vic-tims of cyber dating abuse, however, we focused on the factors that predict perpetration of cyber dating abuse. We explored whether aggression (verbal aggression, physical aggression, anger and hostility), romantic jealousy (emotional, cognitive and behavioral jealousy), and gender predicted perpetration of cyber dating abuse (n = 189). We found that hostility, behav-ioral jealousy and gender significantly predicted perpetration of cyber dating abuse. The findings of this study contribute to our understanding of the psychological factors that drive cyber dating abuse in romantic relationships

Identifying relevant segments of AI applications adopters : Expanding the UTAUT2’s variables

Artificial intelligence (AI) is a future-defining technology, and AI applications are becoming mainstream in the developed world. Many consumers are adopting and using AI-based apps, devices, and services in their everyday lives. However, research examining consumer behavior in using AI apps is scant. We examine critical factors in AI app adoption by extending and validating a well-established unified theory of adoption and use of technology, UTAUT2. We also explore the possibility of unobserved heterogeneity in consumers’ behavior, including potentially relevant segments of AI app adopters. To augment the knowledge of end users’ engagement and relevant segments, we have added two new antecedent variables into UTAUT2: technology fear and consumer trust. Prediction-orientated segmentation was used on 740 valid responses collected using a pre-tested survey instrument. The results show five segments with different behaviors that were influenced by the variables of the proposed model. Once known, the profiles were used to propose apps to AI developers to improve consumer engagement. The moderating effects of the added variables—technology fear and consumer trust—are also shown. Finally, we discuss the theoretical and managerial implications of our findings and propose priorities for future research.peerReviewe