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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 study is to examine the South African banks’ customer due diligence (CDD) practices in the fintech era to mitigate money laundering (ML) risks and ensure financial stability. Financial technologies have brought substantial transformations to the financial services sector. However, such technologies have exposed the sector to emerging risks that threaten the integrity and stability of the financial system globally. Before any bank–customer relationship is established, proper customer background checks must be conducted. These background checks enable financial institutions to validate information provided and ensure customers are properly risk profiled. Failure to risk profile customers could result in financial institutions being used as conduits for ML. Undoubtedly, CDD procedures are pivotal to overall anti-money laundering efforts and curbing financing terrorism in a regulatory framework.

A qualitative research approach was adopted to address the research questions of the study. Given the confidentiality associated with the financial services sector, data triangulation was used in blending mainly secondary and primary data sources. Secondary data sources used in the study were published reports available in the public domain that were corroborated with subject matter experts’ interviews.

Based on the findings of this study, it is concluded that in South Africa, technological solutions have been incorporated into CDD functions, which is now risk-based (enhanced due diligence). Also, legally, South Africa has incorporated the biometrics, integration with Department of Home Affairs and Companies and Intellectual Property Commission databases, customer consent to third-party sources with the Financial Intelligence Centre Act and the Protection of Personal Information Act.

The shift towards digital banking in South Africa results in increased data and dynamic risk profiling. This study advocates a policy shift requiring a risk-based approach to mitigating emerging ML risks (in particular digital laundering), especially in the wake of South Africa’s recent greylisting by the Financial Action Task Force.

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs.

Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers.

This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers.

Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies.

As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.

This paper aims to explore the conditions in a business-to-business-to-consumer (B2B2C) context characterized by new technologies. Innovations enhance disintermediation and pursue sustainability goals that drive customers’ willingness to use eco-friendly delivery options, namely, parcel lockers – in e-commerce and their impacts in terms of communication and transparency along the supply network.

The study conducted an extensive survey in Italy and Germany, collecting 1,010 usable responses. Structural equation modelling was used to analyse the data with the aim of identifying the factors that drive customers’ willingness to use parcel lockers and the effect on customers’ behaviour as determined by the disclosure of information about the environmental performance of different delivery options.

The results highlight several factors affecting the willingness to use parcel lockers, namely, performance and effort expectancy, social influence, technology anxiety, hedonistic motivation and environmental knowledge. The results also demonstrate that the disclosure of information about the environmental performance of different delivery options influences customers’ behaviour.

This paper faces several limitations, mostly related to the focus on just two countries, the use of cross-sectional data and the survey’s explicit reference to just one type of product. Nevertheless, the findings contribute to the discussion on the relevance of information sharing along the supply chain, providing favourable evidence in this regard. It also improves the stream of research concerning technology adoption in the context of e-commerce, highlighting factors that can lead consumers to use eco-friendly self-service technologies.

The results can support companies in understanding how they can design and manage the last mile of delivery to jointly achieve customer satisfaction, process efficiency and superior environmental performance.

This pioneering contribution studies the adoption of delivery solutions for e-commerce and its implications for the supply network.

Customer due diligence measures that are employed in the United Kingdom (UK) to detect and combat money laundering are discussed. The UK adopted a progressive regulatory and enforcement framework to combat money laundering which relies, inter alia, on the use of customer due diligence measures to regulate and curb the occurrence of money laundering activities in its financial institutions and financial markets. However, other regulatory measures that could have contributed to the effective combating money laundering in the UK will not be explored in detail since the article is focused on the reliance and use of customer due diligence measures to curb money laundering activities. Accordingly, the strength, flaws and weaknesses of the UK anti-money laundering regulatory and enforcement framework are examined. Lastly, possible recommendations to address such flaws and weaknesses are provided.

The paper discusses customer due diligence measures that are used in the UK to detect and combat money laundering.

It is hoped that policymakers and other relevant persons will use the recommendations provided in the paper to enhance the curbing of money laundering in the UK.

The paper does not provide empirical research.

The study is useful to all policymakers, lawyers, law students and regulatory bodies in the UK.

The study seeks to curb money laundering in the UK society globally.

The study is original research on the use of customer due diligence measures to detect and combat money laundering in the UK.

The purpose of this study is to examine how servicescapes impact well-being and satisfaction of both hospital customers (patients) and health care professional service providers.

The study investigates how a hospital servicescape impacts two critical outcomes – well-being and satisfaction – of both hospital patients (customers) and health care professionals, who are immersed in that environment.

The hospital servicescape had a greater impact on physical, psychological and existential well-being for professionals than for patients. However, the reverse was true for satisfaction. The new servicescape enhanced the satisfaction and physical and psychological well-being of professionals but only the satisfaction of customers.

The study implications for health care policy suggest that investment in health care-built environments should balance the needs of health care professionals with those of customers to benefit their collective well-being and satisfaction.

Based on the findings, the authors propose that servicescape investments should focus on satisfying the physical needs of patients while also placing emphasis on the psychological needs of professionals.

Health care spending on physical facilities should incorporate careful cost-benefit analysis, ensuring that beneficial features for both user groups are included in new hospital designs, omitting features that are less supportive of well-being.

To the best of the authors’ knowledge, this study is the first to compare the impact of the same real-life servicescape on the satisfaction of both customers and service providers (professionals) and considers the critical health outcome of well-being.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.

Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, the authors develop a three-stage model that accounts for the salesperson’s price decision within the limits of the latitude provided by the firm; the firm’s decision to approve or not approve a sales application; and the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, the authors compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e. Broyden–Fletcher–Goldfarb–Shanno algorithm).

The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives leads to double-digit lifts in firm profits. Moreover, the authors find that the high-risk customer segment is less price-sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.

Substantively, to the best of the authors’ knowledge, this paper is the first to empirically investigate the profitability of analytics-driven segment-level (i.e. discriminatory) centralized pricing compared with sales force price delegation in indirect retail channels (i.e. where agents are external to the firm and have access to competitor products), taking into account the decisions of the three key stakeholders of the process, namely, the consumer, the salesperson and the firm and simultaneously optimizing sales commission and centralized consumer price.

This study examines whether green buildings enjoy more favorable financing terms compared to their non-green counterparts, exploring the presence of a green discount in commercial real estate lending. Despite the extensive research on green premiums on the equity side, lending has received limited attention in the existing literature, even as regulations have increased and ambitious net-zero targets have been set in the banking sector.

In this study, the authors leverage a unique dataset comprising European commercial loan data spanning from 2018 to 2023, with a total loan value exceeding €30 billion. Hedonic regression analysis is used to isolate a potential green discount. Specifically, the authors rely on property assessments conducted by lenders to investigate whether green properties exhibit lower interest rate spreads and higher loan-to-value (LTV) ratios.

The findings reveal the existence of a green discount in European commercial real estate lending, with green buildings enjoying a 5.35% lower contracted loan spread and a 3.92% lower target spread compared to their non-green counterparts. However, this analysis does not indicate any distinct advantage in terms of LTV ratios for green buildings.

This research contributes to a deeper understanding of the interaction between green properties and commercial real estate lending, offering valuable insights for both lenders and investors.

This study, to the best of the authors’ knowledge, represents the first of its kind in a European context and provides empirical evidence for the presence of a green discount.