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This study aims to explore the factors influencing the bottom of the pyramid (BOP) consumers’ adoption and usage intention towards mobile payment (m-payment) to achieve financial inclusion and sustainable development goals.

A qualitative research design is used to explore the enablers and inhibitors that influence BOP consumers’ m-payment adoption and usage intention. To collect the qualitative responses, semi-structured in-depth interviews with BOP respondents were conducted. The thematic analysis using the text mining technique will be used to analyse qualitative data for exploring the predominant factors affecting m-payment adoption intention and usage.

The results suggested awareness, social influences and self-efficacy as crucial enablers and privacy and security risks and vulnerability concerns as crucial inhibitors towards m-payment adoption and usage.

As a novel contribution to the BOP, financial inclusion, sustainable development goals and m-payment literature, this study unfolds several unknown perceived benefits and perceived sacrifices that influence the BOP consumers’ m-payment adoption intention and usage. The study’s findings help the government and banks formulate and implement strategies to achieve financial inclusion among BOP consumers.

The purpose of this study is to consolidate the existing literature on small retail and develop a conceptual framework using thematic analysis.

The relevant set of 224 articles has been obtained from the Scopus database by applying the PRISMA framework. Bibliometric analysis has been performed using Biblioshiny in Bibliometrix and VOSviewer.

Four major themes have been identified within the conceptual structure of the small retail domain, and a conceptual framework has been developed using the interlinkages within the themes. The intellectual structure of the domain has been explored using citation analysis, co-citation analysis and bibliographic coupling. Future research directions are also identified and documented based on the thematic analysis and overall consolidation of the literature.

This is perhaps one of the first attempts to consolidate the published literature on small retail using bibliometric analysis.

This study explores digital transformation in the tea supply chain within developing economies, with a focus on smallholder tea producers in Sri Lanka. Tea is one of the most widely consumed beverages in the world. Among the tea producers, smallholder tea producers account for a substantial portion of total tea production in several countries. Mobile phones play a significant role in providing smallholder producers with access to crucial agricultural information, markets and financial services.

This study adopts a deductive approach, analysing mobile phone ownership, literacy, experience and perception among smallholder tea producers. The chi-squared test of independence and hierarchical clustering methods were used to test the hypotheses and address the research questions.

The study identifies four clusters of smallholder tea producers as Basic Tech Adopters, Digital Laggards, Skeptical Feature Phone Users and Tech-savvy Adopters based on their characteristics towards mobile-based technologies. Approximately 75% of the surveyed sample, which included both tech-savvy and basic-tech adopters, showed a positive attitude toward adopting mobile-based agricultural technologies.

The study suggests developing targeted strategies and policies to enhance the productivity of the smallholder tea production process in developing economies. The study highlights the importance of awareness, access, affordability and availability when implementing digital services for businesses at the base of the pyramid, such as tea smallholdings in developing economies.

The present study aims to address the lack of data-driven empirical studies on the use of mobile phones in smallholder settings. The findings of this study enable the enhancement of entrepreneurship within the tea production supply chain, especially, within stakeholders who deliver digital transformation support services.

After reading this case study, the students will be able to identify the various choices available in social e-commerce using network marketing, interpret data-driven decisions in social e-commerce and evaluate their role in scaling business, analyse cost and revenue management in value segments, evaluate technology adoption among the masses using appropriate communication structures and develop customer relationships and manage their sentiments in the era of social media.

DealShare became a unicorn in 2022 and targeted the rural and low-income groups. Based on a networking model for customer acquisition and a hyperlocal supply chain model, DealShare is increasing its customer base at a rapid pace. However, profitability was still a challenge, and converting high volume into high value continued to be a daunting task. This case study delves deep into the challenges co-founder Sourjyendu Medda and the DealShare team faced. It seeks to address key issues: how should DealShare leverage customer network for faster customer acquisition and how should they increase ticket size and profitability? As a data-driven business, what advantages does DealShare have in influencing customers’ buying behaviour using data? Dependence on social media could have a cascading effect on “word of mouth”. How can they manage customer complaints and increase engagement?

This case study has the potential to be used in different settings. In strategic cost management, this case study can demonstrate strategies for cost management in the value-conscious segment. This case study can be used in marketing management courses while teaching “positioning” in business-to-consumer markets and CRM. For second-year management students, this can be used in entrepreneurship and strategic management courses to demonstrate the network effect in social e-commerce start-up businesses. This case study is also relevant for various course modules in graduate management programmes to demonstrate the power of data-driven decision-making in business.

Teaching notes are available for educators only.

CSS 8: Marketing

This study aims to examine the co-creation of customer experiences at different levels in service ecosystems, analyzing the case of a tourist destination.

A questionnaire was designed based on previously validated scales. The questionnaire was distributed through the social media platforms Facebook and Instagram. The survey yielded 1,476 valid responses for three types of destinations. Structural equation modeling and multigroup analysis were performed to test the hypotheses.

Aggregate service experience and memorable customer experience (MCE) in service ecosystems are determined by customer experiences at a dyadic level. Service experience at the ecosystem level is formed from ordinary experiences at the actor level, while MCE is formed from extraordinary experiences at the dyadic level. The type of ecosystem moderates the relationships between the variables but does not alter the importance of each of them.

The relationship between the co-creation of customer experiences at different levels of service ecosystems (dyadic vs aggregate) is addressed. A relationship is established between the ordinary and extraordinary character of experiences and their memorability at the ecosystem level.

Omni-channel shopping affords consumers a variety of delivery options to receive products based on their preferred times and locations. By considering consumers' contributions (physical, social and attentive efforts) in co-creating delivery services, this study investigates their preferences for parcel delivery.

A scenario-based questionnaire survey is conducted for data collection in Singapore (n = 483). Furthermore, a multinomial logistic regression is performed to assess consumers' choice mode of delivery among five alternatives, that is attended home delivery, unattended home delivery, automated self-collection locker, attended pickup point and click-and-collect.

Compared to attended home delivery, consumers who choose the alternatives are found to be more willing to contribute physical effort but less interested in responding attentively to informational updates. Efforts required for social interactions discourage consumers from choosing attended deliveries, prompting unattended alternatives (e.g. home delivery and self-collection) as more attractive choices. Additionally, socio-demographic factors and product value also influence consumers' preferences.

This study contributes to the literature by integrating the theoretical concept of consumer logistics into omni-channel studies, providing a new approach to examining consumers' channel behaviour. With detailed profiling that links product value and consumers' socio-demographics to their choice mode of delivery, the authors create practical insight into the optimal design of omni-channel distribution systems that best harness consumers' voluntary contributions.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.

Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.

The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.

This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.

Resilience has evolved as a dynamic process in the entrepreneurship field. The purpose of this paper is to outline a comprehensive structure to analyse the patterns and trends in the publications of the existing literature at the junction of entrepreneurship and resilience. With the help of bibliometric and network analysis, this study offers insights into the topic that have not been evaluated and assessed by previous reviews.

A computerised search of 104 papers was performed using the Scopus database, and graphical visualisation of the bibliographic material was developed using VOSviewer software.

This comprehensive bibliometric mapping helps in the graphical visualisation of publication evolution of the domain along with identifying present research trends and possible future directions. There is not much collaborative research in the field, as most prolific thinkers work in isolation or in pairs. Hence, there are limited publications in top-rated journals. Future researchers need to work collaboratively to produce high-quality papers. Developed nations make a sound contribution to the field. The exact significance of resilience in entrepreneurship is yet to be determined due to a wide variety of themes that reflect the multi-disciplinary nature of the domain.

Uncovering the trends and developments of the field, this study provides a global perspective and potential themes lying at the junction of resilience and entrepreneurship. Hence, this study provides a robust roadmap for future researchers interested in this area.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.