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The main objective of this paper is to analyse the relationship between innovation and traditional concepts to explain the phenomenon of traditional food with innovation from a market and consumer behaviour perspective in the Mexican context.

The research is carried out in two phases: (1) analysis of the offer in distribution and (2) consumer research. First, a mixed observation technique in the offer of traditional foods with innovation was carried out. The data were recollected from 24 companies' websites and was complemented with information from main distribution chains of the city of Hermosillo (Mexico). Second, a survey was carried out with 310 Mexican consumers. The data obtained were analysed using bi-variable and multivariable techniques.

The findings from the websites showed that there are 19 traditional products with innovation that are marketed through this medium, while 39 traditional products with innovation are offered in distribution chains. Of all foods, 61% showed innovations in ingredients and materials. Also, the consumer evaluations identified three segments: the consumers orientated towards innovations, convenience and health (42.2%), those orientated towards sensory innovations (39%), and those more inclined towards innovations in marketing and availability (18.7%).

The research considers a partial perspective of the agri-food chain and not an integral vision, it is limited to a specific area and to certain traditional foods.

The symbiosis between innovation and tradition is identified from the perspective of supply and demand. The trend that exists in the market regarding the types of innovations and the gaps that exist regarding environmental elements are recognized.

The data obtained in the research generate information for business decision-making and entrepreneurship; in addition indicates new dietary and consumption patterns. It also provides knowledge about innovation and tradition, and highlights the relevance of traditional food.

This study tries to fill a gap in the literature by focusing on the market and consumer behaviour perspective for traditional food with innovation.

This study aims to explore factors that affect gendered consumption (male and female), willingness to pay (economic attributes) and the socio-cultural context of Gen-Z consumers towards specialty coffee as compared to other types in Taiwan.

Samoggia and Riedel’s (2018) theoretical framework is adopted to examine the concepts of interest. A mixed method approach comprising interviews and experimental taste tests was used to collect data from Gen-Z specialty coffee consumers in a purposive sampling manner.

The findings suggested the effect of price elasticity of demand where specialty coffee was perceived as an expensive commodity by young consumers, and hence, not a regularly purchased item. Nevertheless, specialty coffee was linked to health benefits, and a signal for conspicuous consumption – where café experiences facilitated self-promotion on sites like Instagram and Facebook. Finally, the findings alluded to a potential gender effect, with more female young consumers likely to consume specialty coffee as compared to their male counterparts.

This study is located within the context of Taiwan, which has been a tea-dominated consumption landscape for numerous decades. The use of an experimental design also presents a unique angle to elucidate sensory elements surrounding specialty coffee as a research design for Gen-Z research projects. The study points to the relevance of social context in the consumers’ behavioural patterns, which has been largely implicit within consumer behaviour scholarship.

The purpose of this study is to identify the knowledge gap and future opportunities for developing mobile recommender system in tourism sector that lead to comfortable, targeted and attractive tourism. A recommender system improves the traditional classification algorithms and has incorporated many advanced machine learning algorithms.

Design of this application followed a smart, hybrid and context-aware recommender system, which includes various recommender systems. With the recommender system's help, useful management for time and budget is obtained for tourists, since they usually have financial and time constraints for selecting the point of interests (POIs) and so more purposeful trip planned with decreased traffic and air pollution.

The finding of this research showed that the inclusion of additional information about the item, user, circumstances, objects or conditions and the environment could significantly impact recommendation quality and information and communications technology has become one part of the tourism value chain.

The application consists of (1) registration: with/without social media accounts, (2) user information: country, gender, age and his/her specific interests, (3) context data: available time, alert, price, spend time, weather, location, transportation.

The study’s social implications include connecting the app and registration through social media to a more social relationship, with its textual reviews, or user review as user-generated content for increasing accuracy.

The originality of this research work lies on introducing a new content- and knowledge-based algorithm for POI recommendations. An “Alert” context emphasizing on safety, supplies and essential infrastructure is considered as a novel context for this application.

The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.

Some studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.

This study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.

This study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.

Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.

Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.

The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.

This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.

Customer experience management (CXM), which aims to achieve and maintain customers' long-term loyalty, has attracted the attention of many organizations. Improving customer experience management in organizations requires that, first, their relevant capabilities be evaluated. The present study aimed to offer a set of key performance indicators for evaluating customer experience management in commercial banks.

The study, first, attempted to identify the components of evaluating customer experience management by reviewing the related literature and conducting interviews with experts. Then, the extracted components were transformed into assessable metrics using the goal question metric method, and the key performance indicators relevant to customer experience management in commercial banks were selected according to the experts' opinions and the Fuzzy Delphi method.

According to the findings of the study, 21 key performance indicators were identified for customer experience management in commercial banks, and customer satisfaction, the mean number of calls to resolve an issue in customer journey touchpoints, the NPS, and the ratio of the budget allocated to the CXM department to the budget of the marketing department were found as the most significant performance indicator according to banking experts.

The present study was among the first research projects intended to evaluate CXM and offer key performance indicators that could help the managers of commercial banks assess the maturity levels of their CXM.

Sports leagues, such as Major League Soccer (MLS), aim at expanding their audience at global level through alternative media other than television (TV). Brazil stands out among football media consumer audiences as one of the main markets worldwide. Brazilian MLS consumers play the role of fans to converge between TV media and digital platforms, in a phenomenon that has been called Social TV.

The aim of the current research is to investigate how Brazilian MLS fans' consumption process is established through Social TV; it was done based on netnography performed between 2018 and 2020.

Results have indicated that Social TV is a catalyst of practices associated with fan culture: cultural convergence, technologies appropriations, poaching experiences and production of a collective intelligence.

Current research reinforces how ethnography methodology has been gaining room as likely consumer market research, working as alternative method based on the prevalence of focus group and survey techniques.

Social TV phenomenon presents itself as a possibility to expand and direct marketing strategies focused on sports management, just as the media often consumed by fans.

From the results, it is possible assuming that connections between fans are punctually guided by their relationship with the cultural object consumed by them in a network relationship whose actors deindividualize sociocultural practices such as consumption. Thus, the main contribution of the study lies on identifying how fan culture can be autonomously established in the market arena in comparison to other cultures.

Using the data set about the restaurants from different countries and their customer's feedback, the purpose of this paper is to address the following issues: in the restaurant industry, how have user behavior and preferences changed during the COVID-19 restrictions period, how did these changes influence the performance of recommendation algorithms and which methods can be proposed to improve the quality of restaurant recommendations in a lockdown scenario.

To assess changes in user behavior and preferences, quantitative and qualitative data analysis was performed to assess the changes in user behavior and preferences. The authors compared the situation before and during the COVID-19 restrictions period. To evaluate the performance of restaurant recommendation systems in a non-stationary setting, the authors tested state-of-the-art collaborative filtering algorithms. This study proposes and investigates a filtering-based approach to improve the quality of recommendation algorithms for a lockdown scenario.

This study revealed that during the COVID-19 restrictions period, the average rating values and the number of reviews have changed. The experimental study confirmed that: the performance of all state-of-the-art recommender systems for the restaurant industry has significantly degraded during the COVID-19 restrictions period; and the accuracy and the stability of restaurant recommendations in non-stationary settings may be improved using the sliding window and post-filtering methods.

The authors propose two novel methods: the sliding window and closed restaurants post-filtering method based on the CatBoost classification model. These methods can be applied to classical collaborative recommender algorithms and increase the value of metrics under non-stationary conditions. These methods can be helpful for developers of recommender systems and massive aggregators of restaurants and hotels. Thus, it benefits both the app end-user and business owners because users honestly rate restaurants when they receive good recommendations and do not downgrade because of external factors.

To the best of the authors’ knowledge, this paper provides the first extensive and multifaceted experimental study of the impact of COVID-19 restrictions on the effectiveness of restaurant recommendation systems in different countries. Two novel methods to tackle restaurant recommendations' performance degradation are proposed and validated.

利用关于不同国家餐厅及其顾客反馈的数据, 我们探索了以下问题：(i) 在餐饮行业, 用户行为和偏好在COVID-19限制期间如何改变, (ii) 这些变化如何影响推荐算法的性能, 以及 (iii) 可以提出哪些方法来改进封锁情景下的餐厅推荐质量。

为了评估用户行为和偏好的变化, 本研究进行了定量和定性数据分析, 对比了COVID-19限制期前后的情况。为了评估非稳态环境中餐厅推荐系统的性能, 我们测试了最先进的协同过滤算法。我们提出并研究了一种基于过滤的方法, 以提高封锁情景下推荐算法的质量。

研究发现, 在COVID-19限制期间, 平均评分和评论数量发生了变化。实验研究证实：(i) 在COVID-19限制期间, 所有最先进的餐厅行业推荐系统的性能显著下降; (ii) 使用滑动窗口和后过滤方法可以改进非稳态环境下餐厅推荐的准确性和稳定性。

我们提出了两种新方法：基于CatBoost分类模型的关闭餐厅后过滤和滑动窗口方法。这些方法可以应用于经典的协同过滤推荐算法, 并在非稳态条件下提高指标值。这些方法对于推荐系统的开发者和大规模餐厅和酒店聚合平台都有帮助。因此, 这对于应用的最终用户和企业主都有好处, 因为当用户得到良好的推荐时, 他们会诚实地对餐厅进行评价, 而不会因为外部因素降低评分。

本文首次提供了COVID-19限制对不同国家餐厅推荐系统有效性影响的广泛多方面的实验研究, 并提出和验证了两种解决餐厅推荐性能下降问题的新方法。

This study aims to examine entertainment TV shows' social media accounts to theoretically and practically explore the relationship between social media engagement and the performance (represented by ratings) of such shows.

By using the data of a popular TV show in the USA, The Voice, the present study examined the messages on the Facebook fan page of the show and how these messages correlated with the ratings of the show. Social media usage data in the course of three seasons (Seasons 10–12, 82 episodes in total) were collected from Facebook (N = 1,192,722 messages). Both regression and sentiment analysis were performed.

Overall, the findings revealed positive relationships of TV show ratings with both passive social media engagement (Facebook likes) and the number of official posts. However, active social media engagement was not positively related to show ratings.

By enhancing understanding of audience engagement with social media, our research extends knowledge related to the nature and development of viewer involvement with entertainment across different media platforms. Our results also help clarify how interpersonal communication (social media comments) and mass communication (TV programs) intersect. Practically, the findings could be applied to improve the interaction of TV audiences with show content, provide insights into the future of social TV development and inform decision-making amongst TV industry professionals.

This study investigated how consumers' confidence in medicine and health information seeking and usage could be related to purchase intentions and satisfaction.

A panel of 18 food supplements consumers were interviewed using soft laddering. Qualitative data were coded and used to develop a structured survey. Participants (N = 363) were recruited on a voluntary basis among the customers of an Italian company in this sector. Hypotheses were tested by linear regressions and generalized models.

Results showed that consumers' confidence in medicine interacted with health information seeking and usage influencing both purchase intention and satisfaction. Consumers with high confidence behave differently from those with low confidence.

The authors used a sample based on one company's customer base.

Companies should segment their customers based on their level of confidence in medicine and adopt different marketing strategies for different segments.

A broader knowledge of consumers' attitudes towards food supplements and medicines can improve the public policies aimed at increasing quality of life.

From a theoretical viewpoint, findings suggest to consider consumers' confidence in medicine along with other subjective and contextual variables in socio-cognitive models aimed at explaining food supplements' consumer behavior. From a marketing viewpoint, results suggest to consider confidence in medicine as a precious variable in segmentation strategies. While some communication strategies are valid for all customers (i.e. using experts as advisors, using scientific contents in ads), others (i.e. relying on the advice of trustworthy people, explaining the consequences of consumption) were proved to have different impact on consumers depending on their degree of confidence in medicine.