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The purpose of this paper is to identify the dimensions of customer expectations and study the moderating role of demographics in the context of fresh food retail.

A structured questionnaire was designed using extant literature review followed by expert opinions. The survey was conducted among the customers in the twin cities of Maharashtra in India. The factors of customer expectations were identified using exploratory factor analysis (EFA) and further confirmed using confirmatory factor analysis in SPSS and AMOS, respectively. The significance of the customer expectations on customer satisfaction was studied using structural equation modeling. Subsequently, the role of demographics was studied using two-step cluster analysis and multigroup moderation.

During EFA three factors emerged, namely, product-related features, in-store quality and store support services. Structural model evaluation found product-related features and in-store quality significantly influencing the customer satisfaction, while store support services were found as a non-significant factor in the region studied. Further, using cluster analysis customers were segregated into three groups, namely, traditional, autonomous and premium customers, where the premium customers were found to prefer the store support services on a higher scale, and similar results were obtained using multigroup moderation. Demographics, namely, gender, age, respondents’ income and marital status moderated for product-related features and in-store quality. Interestingly, respondents’ income also moderated for the store support services.

The findings provide directions for store managers of the fresh food category to align supply chain decisions with the unique requirements of customers considering their socio-economic characteristics.

On the basis of social exchange theory, the authors found that in a mutually beneficial relationship, concerning the value proposition, retailers need to address the requirement of the different income group customers for store support services.

A review of past scholarly work on pricing issues in hospitality has revealed a lack of focus on customers’ demographic profiles. However, research in other disciplines reveals that understanding price perception differences among groups of customers with different demographics, including culture, is an important consideration when offering pricing strategies. The purpose of this paper is to contribute to the body of pricing research by exploring the effect of hotel guests’ demographics on their perception of hotel room prices.

Through Qualtrics, data were collected from 414 respondents who stayed at a mid-scale hotel within the past 24 months. The respondents’ perceived value (PV), perceived fairness (PF) and willingness to pay (WTP) for hotel rooms were examined with MANOVA and ANOVA tests to determine the effects of customer demographics on these variables.

Age, gender and marital status showed a significant effect on PV while age, gender and culture significantly affected PF. However, none of these variables significantly affected WTP. The culture of origin and the culture raised-in influenced PV, PF and WTP similarly.

This study reconciles several divergent results from previous studies and extends the scope of others by introducing different scenarios to each of the three dependent variables. To the best of the authors’ knowledge, it is also the first research study on this subject to evaluate more than two cultures and their effects on the independent variables.

Restaurants typically have small profit margins and with the pressure of increasing food and labor costs, management is looking to revenue as a way to maintain and drive profits. One technique to increase revenue is through revenue management practices, but management needs to be aware of their customers’ reactions to these practices prior to implementation. The paper aims to discuss this issue.

This study utilizes linear regression to determine the impact of select restaurant revenue management practices, customers’ familiarity with revenue management in general and in restaurants specifically, and customers’ demographics on perceived fairness of revenue management practices in casual and fine-dining restaurants.

Results indicate that customers find certain restaurant revenue management practices, such as charging premium prices on certain days of the week, fair in both casual and fine-dining restaurants, while others are not in either. Non-refundable reservation fees were found to be fair for fine-dining establishments only. Increased familiarity with restaurant revenue management was associated with higher perceptions of fairness for both casual and fine dining. Age was the only demographic studied that affected perceived fairness.

This study is the only known study to simultaneously evaluate the impact of price and duration restaurant revenue management techniques in combination with customer demographics and revenue management familiarity on consumer perceptions of fairness.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

To be a dominant company (in other words, a long‐term successful company), it is an enormous task to build brand loyalty, to reach brand loyal customers, and to give those customers’ product satisfaction. The purpose of this study was to identify clothing brand loyal customers regarding their buying behavior, self‐image, and demographics. Also, brand loyal customers’ post‐purchase outcomes based on clothing attributes were investigated. The questionnaire was based on a self‐administrated pilot study and included measuring brand loyalty, consumer decision making, and demographics. Data were from 328 questionnaires completed by adult women living in Seoul, Korea. The results showed that in purchasing tee shirts, 24.4 percent of the sample were brand loyal customers, 42.2 percent were brand loyal customers in purchasing trousers and 38.7 percent were brand loyal customers in purchasing jackets. The multiple discriminant analysis indicated several significant variables for profiling brand loyal customers and non‐brand loyal customers. High price, brand loyal customers and low/medium price, brand loyal customers significantly differed in post‐purchase satisfaction.

The purpose of this paper is to compare customers' perceptions of factory outlet stores (FOS) versus traditional department stores (TDS), and their purchasing preferences, related to demographic profiles.

Data were collected by a mall intercept survey from 205 shoppers in a New Zealand city across a range of demographics. Factor analysis measured their perceptions of factory outlets and TDS with respect to a number of variables, and one‐way ANOVA and t‐tests were used to investigate the nature and significance of the observed differences.

Four key factors exert critical influences on customers' perceptions: in‐store customer service, brand images, physical features, and price and promotion. FOS are perceived to have comparatively lower prices and more attractive promotions than TDS, which in turn have competitive advantages in terms of the other three factors. Gender, education and income also affect store choice, but age has no discernible effect on perceptions of the two types of outlet.

TDS should maintain their competitive position by continuing to offer attractive physical features, good in‐store customer service and reputable branded products, while FOS need to learn from the competitive disadvantage of TDS and enhance their current perceived competitiveness on price and promotions.

Previous research studies have tended to pay little attention to demographics and to focus on large economies; this paper addresses both deficiencies.

The present paper aims to investigate the effect of network provider, customer demographics, customer satisfaction and perceived switch costs on churn in the mobile telecommunications market.

The study is carried out as a longitudinal, two-wave study of mobile telecommunications customers in Norway: n=1,499 (wave 1) and n=976 (wave 2). Churn is measured as change in the mobile network provider between the two waves. The data are analysed as a logistic regression with the independent variables provider, gender, satisfaction, switch costs and age.

The analysis shows significant effects of provider, satisfaction, switch costs and age and of the interaction between satisfaction and provider. Gender has no significant effect on churn. Provider effects are interpreted as effects of brand image since other known influences on churn (satisfaction, switch costs and demographics) have been controlled for in the design.

Further research is necessary in order to single out which brand aspects are responsible for the effects of brand ownership and to ensure the generality of the findings outside Scandinavia.

The findings indicate that a strong brand image makes a company less susceptible to customer churn caused by low satisfaction.

The relation between brand ownership and churn in the mobile telecommunications sector has not been reported previously.

The recent economic turmoil in Indonesia has hit the financial service sector hard. Consumer confidence in banks is low and institutions are having to work harder than ever to recruit and retain their customers. In this article the potential for banks to utilize benefit segmentation to assist them in this context is explored. It will be argued that for benefit segmentation to offer any real utility in this context, a link must be found between benefits and general observable characteristics, such as demographics. To ascertain whether such a link exists, a primary study of 1,000 individuals was conducted in the city of Bengkulu, Southwest Sumatra. As the results will show, while discrete bundles of benefits were identified, they would appear generally unrelated to consumer demographics.

To examine the variables that influence micromarketing merchandising in the apparel industry in order to help new retailer understand the importance of micromarketing merchandising.

A model was developed showing the different variables that influenced micromarketing merchandising. General merchandising managers of 20 US‐based apparel retail chains were interviewed using a questionnaire developed after analyzing the available literature. A qualitative method of data analysis was conducted and the model was revised based on the findings of the research.

A qualitative analysis of the transcribed interviews indicated that assortment, demographics, pricing and customer loyalty were the primary variables that effected micromarketing merchandising in the apparel retail industry. The sub‐variables in the study included lifestyle, ethnicity, store size and location, and customer service.

The research was limited to US‐based apparel retailers. Future research could be directed towards in‐depth quantitative analysis of each variable influencing micromarketing merchandising.

The results of this study could be used by managers of retail chains to understand the various variables that need to be considered while micromarketing merchandising for their store. Based on the area the store is located in, the importance of each variable can be adjusted to best suit specific stores.

Understanding the importance of micromarketing merchandising can help new retailers study their consumers based on the important dimensions reported in this research and buy the right product for their target consumers.

This research aims to comprehend the factors that impact the emerging inclination of consumers toward mobile finance technology (fintech) services over banking institutions. The study focuses on users' demographics and psychographics to delineate their unique segments and profiles.

The study proposes a segmentation and profiling framework that includes variance analysis, two-step cluster analysis and pairwise statistical tests. This framework is applied to a dataset of customers using a range of mobile fintech services, specifically robo-investment, peer-to-peer (P2P) payments, robo-advisory and digital savings. The analysis creates distinct customer profile clusters, which are later validated using pairwise statistical tests based on segmentation output.

Empirical results reveal that P2P payment service users exhibit a higher frequency of usage, proficiency and intention to continue using the service compared to users of robo-investment or digital savings platforms. In contrast, individuals utilizing robo-advisory services are identified to have a significantly greater familiarity and intention to sustain engagement with the service compared to digital savings users.

The findings provide financial institutions, especially traditional banks with actionable insights into their customer base. This information enables them to identify specific customer needs and preferences, thereby allowing them to tailor products and services accordingly. Ultimately, this understanding may strategically position traditional banks to maintain competitiveness amidst the increasing prominence of fintech enterprises.

This research provides an in-depth examination of customer segments and profiles within the mobile fintech services sphere, thus giving a nuanced understanding of customer behavior and preferences and generating practical recommendations for banks and other financial institutions. This study thereby sets the stage for further research and paves the way for developing personalized products and services in the evolving fintech landscape.