Search results

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.

The purpose of this paper is to discover profiles of organic food consumers in Lebanon by performing a market segmentation based on lifestyle and attitude variables and thus be able to propose appropriate marketing strategies for each market segment.

A survey, based on the use of closed-ended questionnaire, was addressed to 320 organic food consumers in the capital Beirut, in February and March 2014. Descriptive analysis, principal component analysis and cluster analysis (k-means method) were performed upon collected data.

Four clusters were obtained and labelled based on psychographic characteristics and willingness to pay for the most purchased organic products. “Localist” and “Health conscious” clusters were the largest proportion of the selected sample, thus these were the most critical to be addressed by specific marketing strategies, emphasising the combination of local and organic food and the healthy properties of organic products. “Rational” and “Irregular” cluster were relatively small groups, addressed by pricing and promotional strategies.

This is the first study attempting to segment organic food consumers into different categories in a developing country as Lebanon.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

The purpose of this paper is to address reasons for consumer investment in socially responsible investment (SRI) profiled mutual funds. Specifically, the paper deals with the relative influence of financial return and social responsibility on the decision to invest in SRI profiled mutual funds.

A cluster analytic approach was used where 563 SR‐investors were classified into different segments based on their perception of importance of financial return and social responsibility. Furthermore, discriminant analysis and chi² tests were used to profile the segments.

Three segments of SR‐investors were formed. The “primarily concerned about profit” SR‐investors value financial return over social responsibility. The “primarily concerned about social responsibility” value social responsibility over financial return. The “socially responsible and return driven” SR‐investors value both return and social responsibility when deciding to invest in SRI. The segments displayed distinct differences with regard to various profiling variables.

As respondents were generated from one SRI provider, it is possible that the respondents are not fully representative of all SR‐investors.

Since there are segments of SR‐investors that invest in SRI because of different reasons, there is an opportunity for SRI providers to target and adapt communication to certain segments.

For both academia and the SRI industry this study provides useful knowledge on how private SR‐investors handle the issue of financial return and social responsibility when investing in SRI. This understanding of the differing motivations of the SR‐investor also holds practical importance for developing appropriate marketing strategies within the SRI industry.

The purpose of this paper is to examine the influence of sensory attributes of milk chocolate on consumers’ emotions and their hedonic ratings using three commercial brands of milk chocolate.

Quantitative descriptive analysis by a trained panel (n=8) was performed to obtain the sensory evaluation of the samples. A group of 127 consumers evaluated the samples to indicate their hedonic ratings (nine-point hedonic scale) and emotions (EsSense Profile®).

The sensory profiles for the three chocolate types showed clear differences. EsSense Profile® methodology revealed that each type of chocolate had a distinct emotional profile. The premium brand was associated with the highest number of positive emotions, whereas the traditional brand was associated with most of the negative emotions (“bored”, “disgusted” and “worried”). The drivers of liking were mainly positive and unclassified emotions. Also, gender differences in emotional profiling were found.

This study illustrates that sensory and emotional measurements can contribute to a better understanding of consumers’ hedonic liking. Moreover, gender differences found in emotional profiling should raise awareness that gender may lead to different emotional profiling. These gender differences are of interest to food companies, for instance, for food product development or marketing purposes.

This study further contributes to the growing literature on emotions. By combining sensory evaluation by a trained panel and emotional profiling by consumers, this paper explores how combining these measurements can contribute to a better understanding of the drivers of liking for milk chocolate.

This study aimed to identify and profile segments of seaweed consumers in the United Kingdom.

Hierarchical k-means cluster analysis was used to identify consumer segments based on consumers' self-identity and environmental values. In addition, the study used subjective knowledge, intentions and consumption to profile different consumer segments. The data were collected in 2022 through a consumer survey with a representative sample from the United Kingdom (n = 1,110).

Cluster analysis segmented consumers into three groups: progressive (39%), conservative (33%) and egoistic (28%). The progressive segment was most likely to consume seaweed food products. Consumers in the progressive segment identify themselves as food innovative and healthy; they also highly value the environment and their pleasure. Conservative and egoistic consumers were significantly less likely to consume seaweed food products.

The results suggest that public policy officers and marketers promote seaweed food products by emphasizing biospheric values for innovative (younger) consumers, as well as seaweed’s good taste and nutritional/health qualities.

This study identifies and examines the profiles and characteristics of seaweed consumers based on their values and self-identity. Through this research, the authors have discovered how environmental values and self-identity can effectively group consumers into homogeneous segments. Moreover, the authors have identified a specific consumer group in the UK that is more likely to consume seaweed food products.

Allen and Meyer’s (1990) three component model of organizational commitment is now well accepted in the study of consumer–service provider relationships (Keiningham et al., 2015). Commitment profiling is a “person-centered approach” to commitment (Meyer et al., 2012) which examines groups of individuals who share similar commitment mindsets. The purpose of this paper is to apply commitment profile methodology to the analysis of customer–firm relationships in the context of financial services.

This method was applied with customer data collected as part of a nation-wide panel study of consumer financial service relationships in Canada. In total, 428 banking customers participated in this study.

This study identified five distinct bank customer commitment profiles (fully committed, affective commitment dominant, continuance commitment dominant, moderately committed and uncommitted) that varied in both size and behaviors and intentions.

This is an exploration of commitment profiling as a technique to understand the ways in which consumers differ in terms of their commitment mindsets and behavior. It has application to a wide range of service relationships beyond financial services.

This has applications for market segmentation on the basis of customer commitment mindsets in many service sectors, but banking in particular. Since financial institutions have adopted various techniques to measure customer lifetime value (CLV), it would be appropriate to understand how various commitment profiles (segments) are linked to CLV.

While commitment profiling is a well-developed approach in understanding the nature of the firm–employee employment relationships, this is an early and exploratory attempt at applying this method in the context of a customer–financial service provider marketing relationship. This is a novel way of understanding bank customer segments in terms of their felt commitment to the financial institution with which they do business.

The purpose of this paper is to study the green marketing consumer in the Kingdom of Bahrain through using the ecologically conscious consumer behavior (ECCB) model and profiling green consumers in the kingdom.

Using questionnaire survey, the influence of demographic characteristics (gender, age, education, and household monthly income) and ECCB on green marketing consumers has been tested through factor analysis, regression analysis, ANOVA, and K‐means clustering. The sample consisted of 241 consumers.

The findings of this study provide some understanding to the service providers and the government on the effect of demographic profile on online shopping. Of consequence, such understanding would help them in finding and implementing suitable strategies to enhance online shopping. Four types of green consumers were identified.

The study described in the paper is the first analysis of the Kingdom of Bahrain green consumers' characteristics through providing a comprehensive understanding by combining the demographic characteristics and the ECCB model and their effect on green shopping. Furthermore, the study offers a profile of green market consumers.

Looking to the future of green marketing, examines the dynamic nature of ecologically conscious consumer behavior. The study also provides a method of profiling and segmenting college students based upon ecologically conscious consumer behavior. Findings indicate that, despite a significant amount of past research attention, demographic criteria are not as useful a profiling method as psychographic criteria. Consistent with past findings, the study indicates that perceived consumer effectiveness (PCE) provides the greatest insight into ecologically conscious consumer behavior. Further, the inclusion of altruism to the profile appears to add significantly to past efforts. Additional constructs examined suggest that environmental segmentation alternatives are more stable than past profiles that have relied primarily on demographic criteria.

In the present era of green consumerism, consumers are shifting towards sustainable consumption with the rising demand of green products. Despite consumers' demand of such products, their attitudes towards eco-friendly practices can neither be the same for different consumer groups nor can be treated as they all are equally green. The purpose of this study is to operationalize the green market segmentation based on demographic, psychographic and behavioural characterization in the Indian context of green consumerism.

The deductive approach of questionnaire survey method has been adapted to collect the responses from convenience sampling of Indian consumers using the measured constructs concerning to green consumer psychology. The data were analyzed by applying multivariate techniques of confirmatory factor analysis (CFA), cluster and discriminant analysis.

The results revealed that the three distinctive set of consumer groups are evolved as “keen greens”, “moderate greens” and “reluctant greens” based on the eight cognitive variables used in this study, namely environmental concern (EC), perceived environmental knowledge (PEK), perceived consumer effectiveness (PCE), perception of eco-label, perception of eco-brand and environmental advertisements, green purchase intention (GPI) and green purchasing behaviour (GPB) in the Indian context.

The research findings may lack its generalizability in the Indian context, as the survey strategy is confined with the most populated territory of India. To provide its robustness, the future studies need more heterogeneous sample across the country. The research findings could provide the key insights into policymakers and enterprises in the framing of marketing strategies to promote green consumerism in the setting of emerging economies.

There is dearth of literature concerning to green consumer segmentation based on the “attitude–intention–behaviour” approach in the Indian context. Therefore, the present study endeavours to manifest a holistic description of green consumer classification based on demographic, psychographic and behavioural characterization. At Prima facie, the study is the first that elucidates consumers' segments profile by incorporating environmental cognitive factors from both the perspectives; “consumers” inner stimuli' and “external green marketing cues”, especially in the indigenous Indian setting.