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This study aims to examine the impact of consumer risk appetite, biases (specifically negative recency bias), and the importance of reviews in enhancing information quality. By analyzing these variables, the authors gain insights into their role in enriching the overall information spectrum available to consumers. The findings contribute to a better understanding of how risk appetite, biases and consumer reviews shape the quality of information.

The questionnaire assessed the relationship between dependent and independent variables by asking participants to rate their experiences in relevant scenarios. Variance-based structural equation modeling with the ADANCO program was used to examine the data. ADANCO software is used explicitly for variance-based structural equation modeling. To evaluate research models and test hypotheses, partial least square path modeling is used.

The efficiency of reviews and ratings is greatly influenced by consumer risk appetite. Businesses should focus on clients who are willing to take risks and balance positive and negative feedback. It is essential to comprehend how customers understand reviews. Credibility is increased by taking biases into account and encouraging unbiased criticism. Promoting thorough reviews strengthens influence. Monitoring and making use of these elements improve online reputation and commercial success.

The research has limitations due to the simplicity of the attributes taken into account and the requirement for a larger sample size. Overcoming barriers to promote consistent client feedback is essential, and tailored emails can help with assessment generation. Increased customer participation in writing evaluations can be achieved by removing obstacles and highlighting the advantages of participation.

Businesses and buyers rely on this “organically” generated content as the basis of their promotional strategy and buying decisions. Most of the research is related to consumer reviews, their behavior and the importance of social validation. However, some critical aspects related to this need further investigation.

Online reviewers' identity information is an essential cue by which consumers judge reviews on ecommerce platforms. However, few studies have explored how prior anonymous reviews and focal reviews affect reviewers' preference for anonymity. The purpose of this paper is to investigate why reviewers seek anonymity in terms of prior anonymous reviews and focal reviews.

Based on restaurant reviews collected from meituan.com, one of the largest group-buying ecommerce platforms in China, this study employed logistic regression to examine how prior anonymous reviews and focal reviews are associated with reviewers' preference for anonymity.

Results show that the volume and sequence of prior anonymous review are positively associated with the likelihood of reviewers' preference for anonymity, whereas focal review valence is negatively correlated with this preference. Focal review length is positively correlated with reviewers' preference for anonymity but negatively moderates the roles of review valence and prior anonymous reviews on this preference.

This study expands the information disclosure literature by exploring determinants of user identity disclosure from a reviewer perspective. This research also offers a methodological contribution by employing a more accurate measure to calculate reviewers' preference for anonymity, enhancing the empirical results. Lastly, this work supplements the online review literature on how prior anonymous reviews and focal reviews are associated with reviewers' identity disclosure.

Motivated by the real-world practice that the boom of the online selling induces a higher product return as well, selecting which online channel mode indicates who takes ownership over the product and thus bears the loss of the product return. This paper aims to seek the optimal online channel modes for the two members in a platform supply chain in the presence of product returns.

This study aims to develop a platform supply chain that consists of one platform company and one supplier. Along with an offline distribution channel, the supplier can choose two alternative online selling modes (i.e. the reselling and agency modes) to sell its product through the online marketplace. This paper applies Stackelberg game to derive the equilibrium with different business scenarios and selects the optimal online channel modes for two parties, respectively. Moreover, this paper extends to a different supply chain with a reverse channel leadership and a different product return policy for testing the robustness.

Several interesting and important results are derived in this paper. Firstly, it is found that the relative pricing are largely relied on the costs of two channels. Secondly, the platform supply chain may benefit from a pure channel rather than the dual-channel when this channel enjoys a relatively low cost and/or a sufficiently high consumer preference. Then, the platform and the supplier act contradictorily when selecting their optimal online channel modes. To be specific, the platform motivates to choose the online reselling mode when both the commission rate and the slotting fee are relatively low, whereas the supplier is likely to select the online agency mode under this circumstance. Finally, a win-win situation in regards to the optimal online channel mode for two parties is achievable with numerical experiments.

Based on the analytical studies, the results derived in the authors’ work can provide managerial insights to assist the supplier and the platform company in determining the operational decision and selecting the optimal online channel mode to deal with consumer returns. In addition, appropriate commission rate along with slotting fee will make both parties achieve a win-win situation in determining their optimal online channel mode.

To the authors’ best knowledge, this paper makes the first move to determine the optimal online channel mode in the content of consumer returns and study how it is affected by different product return policies.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

The main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.

The design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.

The modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.

Business limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.

The modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.

The adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.

The food industry is continuously developing its online services called food delivery applications (FDAs). This study aims to evaluate FDA's importance–performance and identify strategies to maximize its potential gains from a business partner's perspective.

Data are collected from 208 FDA partners in Indonesia. Importance–performance analysis (IPA) is applied to evaluate the FDA feature and extended the theory of potential gain in customer value (PGCV) to achieve potential gains from FDA business partners.

This study provides a clear and measurable direction for future research to develop FDA performance. Owning customer data, revenue sharing and competitive advantage are the most potential gains from joining the FDA from the business partner perspective.

The respondents are restaurants from the micro, small, and medium enterprises levels. Further research should involve middle to upper level restaurants to discover all business partners' perceptions. This will be very helpful for FDA providers interested in improving the best performance for all their partners.

FDA providers must focus on improving and maintaining the features of owning customer data, revenue sharing, competitive advantage, stable terms and conditions, customer interface, building customer loyalty, online presence, user credit rating, promotion and offers, delivery service and sales enhancement to increase consumer satisfaction and meet the expectations desired by business partners.

This research provides a meaningful theoretical foundation for future work. It extends the theory of PGCV using the value of a partner perspective as a substitute for customer value; hence, the authors call it a potential gain in partner value.

Food waste is a severe problem affecting the supply chain due to its significant adverse social and environmental effects. Even if the topic is hotly debated in the literature, there is a lack of research about the success factors influencing food waste prevention initiatives retailers undertake.

The research analyzes how several variables (i.e. product-related variables and technology-enabling variables) might impact the success of the sales of products close to the expiration date that is sold at a discounted price. Data from 390.000 products sold at a discounted price in 2020 and 2021 by a large Italian food retailer were examined with a regression analysis.

The results highlight that both product-related and technology-enabling variables influence the success of food prevention initiatives aimed at selling products close to the expiration date at a discounted price. In particular, the authors stress the importance of digital technologies in supporting food waste prevention initiatives.

The study offers several practical implications for managers in structuring a waste prevention initiative. The introduction of digital technologies, the monitoring of specific variables or the ability to find synergies with other food waste prevention initiatives are discussed to support retailers in reducing food losses.

The paper is focused on the retailer perspective, which is barely investigated due to the difficulty in finding data.

Gamification is a strategic approach employed by practitioners to foster meaningful engagement and enhance the acceptance of recommendations. Gamification affordances (e.g. achievement, self-expression, interaction, and cooperation) catalyze significant psychological processes in consumers, leading to behavioral changes. Despite its application, a gap remains in understanding how these gamification affordances in e-commerce contexts impact customers' perceived values and drive recommendation acceptances.

Employing affordance theory and perceived value theory as our foundation, we have crafted a comprehensive framework that addresses the multifaceted nature of e-commerce gamification, thereby unifying the fragmented knowledge in this area. We implemented a quantitative research design to empirically test the proposed model.

The research reveals that the four principal affordances of gamification – achievement, self-expression, interaction, and cooperation – significantly enrich consumer values across hedonic, utilitarian, and social dimensions. This enrichment facilitates an increased propensity for accepting recommendations.

This study provides a novel lens through which to view the influence of gamification affordances on recommendation acceptance within gamified e-commerce settings. It delineates the effects of each affordance on consumers' perceived value and highlights the pivotal affordances that shape gamified e-commerce experiences. These insights yield actionable strategies for practitioners aiming to refine e-commerce gamification designs and cultivate more engaging consumer interactions.

This study employs the Social Identity Theory to examine the differential effects of personal and social dimensions of fear of missing out (FOMO) on sustainable food consumption (SFC) practices.

An online survey-based empirical study was conducted with 395 respondents. The data were analysed using structural equation modelling and Hayes process Macro in SPSS.

SFC was found to be positively influenced by personal FOMO. Contrary to expectations, social FOMO had a negative correlation with SFC. Social influence and social identity were shown to be positively correlated, whilst the social influence-SFC relationship was favourable. This approach was aided by social identity.

The study supports personal FOMO as an SFC-influencing factor. It evaluates the differential effects of FOMO’s personal and social dimensions on SFC. It also demonstrates that social FOMO negatively affects SFC, contrary to expectations.

The study advises sustainable food firms to reduce personal FOMO via advertising and messaging.

This research is amongst the first to segregate the differential effects of social and personal FOMO regarding SFC behaviour. Research has examined FOMO as a higher-order construct involving social and personal aspects. Second, FOMO is often associated with negative behaviours including social media addiction and substance abuse. This FOMO-related research analyses a desired behaviour.

Electric cars (EC) adoption represents a strategic action aimed at promoting environmental sustainability. Although Millennials and Gen Z represent the greatest potential market for EC, their adoption remains low; thus, this study focused on examining the role of motive in predicting EC adoption intention within these two generations’ population. Built upon the fundamental motive framework, this research explores the motives that lead to EC adoption intention. Subsequently, this study aims to examine the role of performance expectancy as the mediating variable and EC attributes beliefs as the moderating variable that can promote EC adoption intention.

Both exploratory and confirmatory methods were used in this investigation. Using an exploratory approach, this research explores the fundamental motives and the attributes of EC that influence EC adoption intention. Using a confirmatory approach, this research tests the mediating role of performance expectancy. To collect the data, an online survey was administered to 260 young consumers in Indonesia.

The results of PLS-SEM analysis from the data revealed that self-protection, kin-care, status and affiliative motives influence EC adoption. Furthermore, performance expectancy mediates the relationship between self-protection, mate acquisition, affiliative motives and EC adoption intention. Among EC attributes, the short-haul performance strengthens the indirect relationship between affiliative motive and EC adoption intention.

The main limitation of this study is that it only focuses on the practical attributes of EC, whereas psychological attributes that were found to be more influential in consumer’s purchase decisions were not examined.

Marketers need to explore EC attributes that can strengthen the relationship between consumers’ motives and EC adoption intention by increasing consumers’ evaluation of performance expectancy. In this study, marketers can promote short-haul performance, as it will lead to EC adoption for consumers with affiliative motives.

This study ties together two lines of research on the adoption of EC, exploring EC attributes and examining consumers’ motivation to choose EC, especially Millennials and Gen Z. In this way, EC attributes facilitate the fulfillment of consumers’ needs and promote EC adoption intention.