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Prepaid mobile Internet is one of the most profitable services that are composed of multiple attributes. The overall utility of Internet service can be broken down into the sum of the utility of individual attribute levels. Based on the multi-attribute theory, rational consumers choose the service that yields the highest utility from a number of possible alternatives. Determining the optimal attribute levels that satisfy consumers' preferences and maximize the total revenue of the firm is a challenging multi-attribute decision problem for any mobile operator. When designing mobile Internet services, adopting a robust composition of services against different realizations of competitors' strategies can bring advantages for network operators. The purpose of this study is to determine the optimal attribute levels of prepaid mobile Internet packages with the aim of maximizing the total revenue of the firm by considering the paradigms of multi-attribute utility theory about consumer choices and the issue of uncertainty in counterpart services offered by the competitors.

This paper formulates the problem of multi-attribute pricing and design of mobile Internet plans in a competitive environment by developing deterministic and robust scenario-based mathematical models and considering the paradigms of multi-attribute utility theory about consumer choices. The proposed robust scenario-based models are based on three different paradigms, including maximizing expected revenue, minimizing the negative deviation from expected revenue and minimizing the maximum regret. A comprehensive numerical analysis is conducted to evaluate and compare the efficiency of the proposed models.

The evaluations reveal that deploying recourse policy can result in higher revenue for the firm when facing uncertainty. By doing sensitivity analysis, this paper shows that consumer preferences for brand attribute and consumers' purchase frequency can influence the revenue of network operators.

This paper develops a novel deterministic multi-attribute product line design (PLD) model to address the problem of determining the price and composition of prepaid mobile Internet plans. Furthermore, the issue of uncertainty in counterpart services offered by the competitors is studied for the first time in the PLD literature.

This chapter offers a preliminary investigation into the impact of the COVID-19 pandemic on consumer purchasing behaviour within the grocery retail industry and supports evidence that since the pandemic began at the end of 2019, there have been changes in the demands and behaviours of consumers (Donthu & Gustafsson, 2020). Previous research has reported that the pandemic resulted in retail consumers spending less and saving more (Jorda, Singh, & Taylor, 2020), as well as panic buying (Nazir, 2021), both of which initially contributed to the limited availability of goods. This preliminary study reports upon survey data collected from retail consumers and answers the question ‘What were the changes in consumer behaviour in the grocery sector as a result of the COVID-19 pandemic?’ Findings reveal that an increase in online shopping occurred more distinctly during the first of the UK’s lockdowns, which waned over time. Findings also reveal a lower shopping frequency, but higher shopping spends during lockdown, and that social distancing and discipline were key drivers of this behaviour change. Findings also reveal an intention to maintain a combination of new and old shopping behaviours and habits after lockdown, giving rise to the continuing importance of meeting consumers’ grocery needs online as well as in-store. This chapter further discusses the implications arising from the reported findings.

This paper examines the design and evaluation of a TV on Demand (TVoD) system, consisting of a globally accessible storage architecture where all TV content broadcast over a period of time is made available for streaming. The proposed architecture consists of idle Internet Service Provider (ISP) servers that can be rented and released dynamically as the client load dictates. This paper examines issues of resource management and content placement within this Video Content Distribution Network (VCDN). The existing placement algorithm is computationally expensive and in some cases, infeasible to execute within any reasonable length of time. This work proposes a number of new placement heuristics each of which attempts intelligently to reduce the search space so that only the best proxies are considered for replica placement. An extensive evaluation of these placement algorithms is carried out to identify a good placement algorithm without being computationally expensive.

Conceptual and empirical research over the past 20 years has moved the social media (SM) literature beyond the embryotic stage to a well-developed academic discipline. As the lead article in the special issue in the Journal of Research in Interactive Marketing on Cutting-Edge Research in Social Media and Interactive Marketing, this review and agenda article has two key goals: (1) to review key SM and interactive marketing research over the past three years and (2) to identify the next wave of high priority challenges and research opportunities.

Given the “cutting-edge” research focus of the special issue, this review and research agenda paper focused on articles published in 25 key marketing journals between January 2021 and March 2024. Initially, the search request was for articles with “social media, social selling, social commerce” located in the article title, author-selected key words and journal-selected keywords. Later, we conducted searches based on terminology from articles presented in the final review. In total, over 1,000 articles were reviewed across the 25 journals, plus additional ones that were cited in those journals that were not on the initial list.

Our review uncovered eight key content areas: (1) data sources, methodology and scale development; (2) emergent SM technologies; (3) artificial intelligence; (4) virtual reality; (5) sales and sales management; (6) consumer welfare; (7) influencer marketing; and (8) social commerce. Table I provides a summer of key articles and research findings for each of the content areas.

As a literature review and research agenda article, this paper is one of the most extensive to date on SM marketing, and particularly with regard to emergent research over the past three years. Recommendations for future research are integrated through the paper and summarized in Figure 2.

Consumer welfare is one of the eight emergent content areas uncovered in the literature review. Specific focus is on SM privacy, misinformation, mental health and misbehavior.

The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.

The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.

The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.

The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

The transition to omnichannel retail is the recognized future of retail, which uses digital technologies (e.g. augmented reality shopping assistants) to enhance the customer shopping experience. However, retailers struggle with the implementation of such technologies in brick-and-mortar stores. Against this background, the present study investigates the impact of a smartphone-based augmented reality shopping assistant application, which uses personalized recommendations and explainable artificial intelligence features on customer shopping experiences.

The authors follow a design science research approach to develop a shopping assistant application artifact, evaluated by means of an online experiment (n = 252), providing both qualitative and quantitative data.

Results indicate a positive impact of the augmented reality shopping assistant application on customers' perception of brick-and-mortar shopping experiences. Based on the empirical insights this study also identifies possible improvements of the artifact.

This study's assessment is limited to an online evaluation approach. Therefore, future studies should test actual usage of the technology in brick-and-mortar stores. Contrary to the suggestions of established theories (i.e. technology acceptance model, uses and gratification theory), this study shows that an increase of shopping experience does not always convert into an increase in the intention to purchase or to visit a brick-and-mortar store. Additionally, this study provides novel design principles and ideas for crafting augmented reality shopping assistant applications that can be used by future researchers to create advanced versions of such applications.

This paper demonstrates that a shopping assistant artifact provides a good opportunity to enhance users' shopping experience on their path-to-purchase, as it can support customers by providing rich information (e.g. explainable recommendations) for decision-making along the customer shopping journey.

This paper shows that smartphone-based augmented reality shopping assistant applications have the potential to increase the competitive power of brick-and-mortar retailers.

COVID-19 disrupted the lives of consumers across the globe, and the retail sector has been one of the hardest hits. The impact of COVID-19 on consumers' retail choice behaviour and retailers' responses has been studied in detail through multiple lenses. Now that the effect of COVID-19 is abating, there is a need to consolidate the learnings during the lifecycle of COVID-19 and set the agenda for research post-COVID-19.

Scopus database was searched to cull out academic papers published between March 2020 and June 6, 2022, using keywords; shopping behaviour, retailing, consumer behaviour, and retail channel choice along with COVID-19 (171 journals, 357 articles). Bibliometric analysis followed by selective content analysis was conducted.

COVID-19 was a black swan event that impacted consumers' psychology, leading to reversible and irreversible changes in retail consumer behaviour worldwide. Research on changes in consumer behaviour and consumption patterns has been mapped to the different stages of the COVID-19 lifecycle. Relevant research questions and potential theoretical lenses have been proposed for further studies.

This paper collates, classifies and organizes the extant research in retail from the onset of the COVID-19 pandemic. It identifies three retail consumption themes: short-term, long-term reversible and long-term irreversible changes. Research agenda related to the retailer and consumer behaviour is identified; for each of the three categories, facilitating the extraction of pertinent research questions for post-COVID-19 studies.

The purpose of this meta-analysis was to examine the effect of pomegranate juice (PJ) intake on glycemic control in adults.

Materials and methods: PubMed (Medline), ISI Web of Science, Cochrane Library and Scopus databases, measuring glucose and/or insulin and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) in adults, were searched from inception to December 11, 2021. Moreover, to examine whether grouping factors influenced heterogeneity between research results, subgroup analysis was used.

This meta-analysis showed that PJ intake reduced HOMA-IR significantly, especially if =250 mL was used. This reducing effect remained significant in females, nondiabetic patients and unhealthy subjects.

The authors believe the presented data would be highly motivating and of a wide readership for the readers of your journal, and this paper stimulating a surge of research on the impact of PJ consumption on glycemic indices.

Questionnaire studies of knowledge, attitude and practice (KAP) are effective research in the field of health, which have many shortcomings. The purpose of this research is to propose an automatic questionnaire-free method based on deep learning techniques to address the shortcomings of common methods. Next, the aim of this research is to use the proposed method with public comments on Twitter to get the gaps in KAP of people regarding COVID-19.

In this paper, two models are proposed to achieve the mentioned purposes, the first one for attitude and the other for people’s knowledge and practice. First, the authors collect some tweets from Twitter and label them. After that, the authors preprocess the collected textual data. Then, the text representation vector for each tweet is extracted using BERT-BiGRU or XLNet-GRU. Finally, for the knowledge and practice problem, a multi-label classifier with 16 classes representing health guidelines is proposed. Also, for the attitude problem, a multi-class classifier with three classes (positive, negative and neutral) is proposed.

Labeling quality has a direct relationship with the performance of the final model, the authors calculated the inter-rater reliability using the Krippendorf alpha coefficient, which shows the reliability of the assessment in both problems. In the problem of knowledge and practice, 87% and in the problem of people’s attitude, 95% agreement was reached. The high agreement obtained indicates the reliability of the dataset and warrants the assessment. The proposed models in both problems were evaluated with some metrics, which shows that both proposed models perform better than the common methods. Our analyses for KAP are more efficient than questionnaire methods. Our method has solved many shortcomings of questionnaires, the most important of which is increasing the speed of evaluation, increasing the studied population and receiving reliable opinions to get accurate results.

Our research is based on social network datasets. This data cannot provide the possibility to discover the public information of users definitively. Addressing this limitation can have a lot of complexity and little certainty, so in this research, the authors presented our final analysis independent of the public information of users.

Combining recurrent neural networks with methods based on the attention mechanism improves the performance of the model and solves the need for large training data. Also, using these methods is effective in the process of improving the implementation of KAP research and eliminating its shortcomings. These results can be used in other text processing tasks and cause their improvement. The results of the analysis on the attitude, practice and knowledge of people regarding the health guidelines lead to the effective planning and implementation of health decisions and interventions and required training by health institutions. The results of this research show the effective relationship between attitude, practice and knowledge. People are better at following health guidelines than being aware of COVID-19. Despite many tensions during the epidemic, most people still discuss the issue with a positive attitude.

To the best of our knowledge, so far, no text processing-based method has been proposed to perform KAP research. Also, our method benefits from the most valuable data of today’s era (i.e. social networks), which is the expression of people’s experiences, facts and free opinions. Therefore, our final analysis provides more realistic results.

This conceptual paper aims to provide a further understanding of the impact of economic policy uncertainty (EPU), news framing and information overload on panic buying behavior during the COVID-19 pandemic.

Drawing on earlier research and news releases about the COVID-19 outbreak, this paper advances testable propositions based on the protection motivation theory and information processing theory.

This paper infers that the major shift in consumer decision-making towards panic buying is a result of high EPU. International reports have contributed to deepening this uncertainty, and the consequences of this EPU are expected to affect the economic recovery through 2022. Furthermore, the adoption of particular frames of the pandemic has played a key role in the dissemination of misinformation and fake news during the public health crisis and affected purchasing decisions. The study also infers that the perceived threat among consumers is driven by information overload as a source of mistrust towards economic and health information sources.

This paper addresses two theoretical gaps associated with consumer buying behaviour. First, it highlights the impact of EPU, as a macroeconomic indicator, on consumer buying behaviour. Second, this paper is an attempt to integrate theories from different disciplines to foster an adequate understanding of buying behavior during the COVID-19 outbreak period.