Search results

The purpose of this paper, which is based on the UTAUT2 model, is to develop and evaluate a predictive model of customer satisfaction related to mobile commerce (m-commerce) and the willingness to recommend this service to others.

The study was conducted based on a sample of 402 respondents. Confirmative factor analysis was used to evaluate the validity of the model, while structural equation modeling (SEM) was used to test the hypotheses. Finally, artificial neural networks were used to rank the influence of the significant predictors obtained by SEM.

Trust was found to be the most significant driver of customer satisfaction, followed by performance expectancy and perceived value. In addition, affective commitment and satisfaction were identified as the strongest predictors of word of mouth (WOM).

The originality/value of the paper lies in the establishment of the connection between the independent variables of the UTAUT 2 model – trust, satisfaction, affective and continence commitment and WOM. Additionally, it is one of a small number of studies investigating customer commitments and their influence on WOM in m-commerce.

The purpose of this paper is to determine statistically significant drivers of customer satisfaction in mobile commerce and to test the moderating effects of customization on the relationships between customer satisfaction and its predictors.

The sample comprised 224 respondents. Confirmatory factor analysis was used to test the validity of the model, and moderated regression analysis was applied to determine main and interaction effects.

Trust, perceived usefulness, mobility, and perceived enjoyment were found to be significant drivers of customer satisfaction. The results also indicate the statistical significance of two interaction effects: customization moderates the influence of mobility and the influence of trust on customer satisfaction.

The study was conducted in a single time period and in a developing country where m-commerce is still not widely used. Future models should include new variables. Comparison between different age or gender groups would also be useful.

The findings are useful for m-commerce providers who are developing marketing campaigns, where the focus should be on promoting the mobility aspect of m-commerce, in particular its usefulness to consumers and its security. M-commerce activities should be developed and redesigned to better meet consumers’ specific demands and needs.

M-commerce customer satisfaction studies are rare. The developed model has five potential antecedents of satisfaction: trust, social influence, perceived usefulness, mobility, and perceived enjoyment. New insights are provided into the moderating role of customization.

The purpose of this paper is to determine the significant antecedents of peer-to-peer (P2P) m-payment acceptance and explore the moderating effects of gender on the influence of these predictors with regards to intention of using the system.

The research was conducted on a sample comprised of 701 Spanish smartphone users. A multi-group structural equation modeling analysis was used to test the moderating effect of gender with a particular focus on the relationships between the latent variables of the research model.

The study identified significant differences between the two observed groups – the results show that men are more likely to use mobile payments than women and are therefore less influenced by the potential risks involved. In addition, men are more easily influenced by their social environment, whereas women are more influenced by their personal innovativeness.

The study proposes a three-level model, based on an extended TAM model. It is a pioneering study, exploring the effects of gender on P2P m-payment acceptance. Due to its novel value and the potential involved, the results of the study may be of great importance for m-payment providers, particularly in marketing strategy planning and customer segmentation.

The purpose of this study is to examine the components of the utilitarian and hedonistic dimensions of the quality of mobile banking services, their impact on customer satisfaction, as well as the effects of satisfaction on word of mouth (WOM) and intention to use.

The analysis includes 307 mobile banking users in the Republic of Serbia. Reliability analysis, confirmatory factor analysis, structural equation modelling and multigroup analysis are applied in the paper.

The results show that security/privacy, perceived usefulness, perceived ease of use, design and enjoyment are important drivers of customer satisfaction, and that satisfaction has very strong effects on WOM and intention to use. In addition, there are apparent differences in the relationships between the two groups of respondents observed: innovators and followers.

The originality of the paper lies in the innovative model that includes a combination of the TAM model and the utilitarian/hedonistic dimension of the quality of mobile banking services. An element of the study which is of particular value is its segmentation of customers into innovators and followers – dividing respondents into two groups depending on whether they are among the first people in their environment to start using mobile banking services.

The COVID-19 virus pandemic has strongly influenced consumer behaviour worldwide. This paper aims to investigate how risk perceptions and precautions related to COVID-19 virus influence consumer eating habits and consequently, behaviour during shopping for food. Also, research tends to identify changes in consumer eating habits resulting from the current pandemic situation.

In total, 237 consumers from Serbia were online surveyed during November 2020. Starting from the methodology of the SOR model (Mehrabian and Russell, 1974), the questionnaire consists of seven-point Likert scale statements that measure risk perceptions and precautions as stimulus (S), eating habits as an organism (O) and food choice, precautions during shopping for food and food purchasing patterns as a response (S).

Research results confirm the difference in consumers' eating habits during and before a pandemic. Also, results indicate that perceived risk and precautions related to the COVID-19 virus have a statistically significant influence on consumers' eating habits which have changed during a pandemic, finally resulting in significant effects on consumers' food shopping behaviour.

The main limitations of this study are observing only a few aspects related to COVID-19 virus pandemic and consumer food shopping behaviour, as well as measuring precautions, perceived risk and food shopping behaviour at one point in time besides the fact that pandemic situation constantly changes.

The study indicates that food manufacturers should pay attention to the consumers' eating habits and food shopping behaviour changes under the circumstances of COVID-19 virus pandemic. Identified changes can be used as opportunities to gain a competitive advantage on the market.

The purpose of this paper is to identify the antecedents of satisfaction and revisit intentions for full-service restaurants. The economic crisis is known to have had a deep impact on consumer behavior with regards to restaurants and this study is primarily focussed on exploring the effects on the traditional antecedents of satisfaction and revisit intentions as full-time restaurants attempt to reposition their offer.

The study involved 218 respondents who were guests of full-service restaurants. In terms of statistical analyses, confirmative factor analysis was used to test the suitability of the proposed model for measuring satisfaction and revisit intentions, while structural equation modeling was employed to analyze the relationship among variables of the conceptualized model.

The results confirmed the significant impact of atmosphere and quality of interaction on guest satisfaction. It was interesting to notice that perceived price has no statistically significant impact on guest satisfaction. In addition, satisfaction, along with atmosphere and perceived price emerged as a significant trigger of revisit intentions. While measuring impact of atmosphere and perceived price on revisit intentions, both direct and indirect effects were measured.

This study emphasizes in particular, the significance of revisit intention as an important behavioral reaction and reveals its antecedents. In a limited number of studies, models for understanding triggers of revisit intention were developed as separate variables, rather than as components of behavioral reactions. In that context, the tested model identifies causal relationships between latent variables. Restaurants involved in the survey are full-service restaurants dealing with the changing behavior of guests who are heavily impacted by the economic crisis. Guests show a tendency to reduce their visits to restaurants in these circumstances, forcing restaurants to reformulate their service offering in order to align with changing customers’ needs.

The aim of the research is to determine the effects of the four dimensions of customer values that the study identifies, namely, emotional value, social value, functional value and monetary value, on attitudinal loyalty and behavioral intentions. The concept of perceived value is increasingly becoming a subject of interest for authors and researchers in the field of services marketing. Perceived value is considered to be a significant predictor of behavioral intentions. The manner in which consumers perceive the value of a product or service that is offered affects the level of their loyalty. In this context, an analysis of perceived value represents a very important activity in terms of customer-orientated management. Quite frequently, customer value is seen as a multidimensional construct that includes several components.

The survey was conducted via a sample of 246 respondents – users of mobile telecommunication operators. Each construct of the suggested model was measured with three to five statements. The respondents expressed their level of agreement with statements on a 7-point Likert scale. The fit of the model was tested by using confirmative factor analysis, while the relationships hypothesized in the study were tested by using structural equation modeling.

By applying structural equation modeling, the significance of the influence of emotional and social values on attitudinal loyalty, as well as the significant effects of functional and monetary value on behavioral intentions, was confirmed. Furthermore, the strong influence of attitudinal loyalty on behavioral intentions was also identified. The results clearly reveal that in terms of the intensity of the effect, emotional value stands out as the strongest antecedent of attitudinal loyalty. Emotional value has a significant indirect effect on behavioral intentions through attitudinal loyalty.

The originality of the study is invoked through identifying four key components of perceived value. The concept of perceived value has only been analyzed by means of its emotional, social, functional and monetary dimension in a small number of prior studies. This paper identifies theoretical, as well as managerial, implications. From a theoretical aspect, causal relationships among viewed variables of the conceptualized model were determined. From the managerial perspective, the results obtained indicate that it is important that mobile operators primarily build a strong emotional connection with their customers because the emotional component of the perceived value has the greatest potential for generating long-term relationships. The findings of this research assist managers employed within mobile network operators to understand the role of various components of customer value in the process of creating customer loyalty.

The purpose of this paper is to identify antecedents and consequences of customers’ emotional reactions in the banking industry. In this context, a model based on segmenting all variables into three segments is proposed.

The research was conducted on a sample of 211 respondents. Data analysis was conducted in AMOS 18. In order to assess the overall model fit, confirmative factor analysis (CFA) was used. To test the research hypotheses, a structural equation model was used.

The research results indicate that trust, social bonds, image and service quality are statistically significant drivers of satisfaction. Of these variables, only service quality has no significant impact on affective commitment. Trust stands out as the variable with the greatest impact on customer emotional responses. Satisfaction is also an important determinant of customers’ behavioral intentions.

Results of the present study indicate that management should pay considerable attention to strengthening intangible elements that imply the development of long-term relationships with clients.

By designing a new model that includes antecedents and consequences of emotional reactions of customers, the present research contributes to theory in the field of services marketing. The model is characterized by its three-tier structure, whereby satisfaction and affective commitment occupy a central position.

With the expansion of dining out, visiting ethnic restaurants is becoming one of dominant trends worldwide. Given the fact that ethnic restaurants involve a number of peculiar elements that are not present when visiting regular “local” restaurants, the purpose of this paper is to identify the key determinants of choosing a particular ethnic-themed restaurant, as well as factors that have an impact on an ethnic restaurant’s perceived image.

Research was done by conducting two separate studies. The first study identified factors that were generally the most significant to respondents when making a choice of an ethnic restaurant in which to dine. For the second study, a new sample of respondents – consisting of those who had just visited such an establishment – was used in order to determine which factors have the most significant impact on creating an image of ethnic restaurants. The research was therefore directed toward those restaurants where the customers can experience a unique atmosphere and ambiance that are synonymous within a given culture.

The results obtained from the first study show that quality of food and price are the two most significant factors that determine which restaurant will be visited. On the other hand, the interior and exterior ranked as the two least important factors. In addition, the second study revealed that following the visit, the strongest impact on the restaurant’s image was its interior. Following interior, service quality surfaced as the most important antecedent of perceived image, while the impacts of price and exterior were weaker, but still statistically significant.

The study itself was done in two stages. In the first stage the analytical hierarchical process (AHP) model was used for ranking factors significant for choosing an ethnic restaurant, while in the second stage, structural equation modeling (SEM) model was used to identify triggers of perceived image after a visit to an ethnic restaurant. Future research should utilize both models to determine antecedents of the variables used. Apart from this, it is recommended to employ new variables that further explore the uniqueness of the ethnic restaurants.

One of the main contributions of this work is related to combining of the AHP and SEM models, with the objective of completing a comparative results analysis, identifying in the process the positive aspects of both models and building a foundation for their coordination in terms of future use. This is significant, given that only a few prior studies have used such synergy.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.