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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.

The purpose of this paper is to analyse the fit between the strategy of service centres and their business model (BM) and to identify the BM components' characteristics and links that allow it to stand out in terms of service delivery and business performance.

This study applies an inductive qualitative multiple case study approach through the empirical analysis of top-performing Italian service centres operating in the Medium–Heavy Commercial Vehicle sector.

Research findings underline that the BM components of top performers are consistent amongst each other and with the adopted strategy and make a positive impact on the firm's performance. In particular, top performers are characterised by a solid financial structure based on equity, formalised and flexible organisational structures and processes, clarity in strategic direction and long-term orientation, grounded capabilities, competences and skills, trustful relationships with main service partners and a comprehensive set of managerial mechanisms.

This paper presents some limitations, typical of qualitative research based on case studies. Future works may include other dimensions of performance for identifying top performers, and extend the empirical analysis to different sectors and national contexts.

This paper supports the relevance of contingency theory – particularly the strategy-structure-performance paradigm – in the analysis of the role of a BM in successful servitization strategies of service centres. It highlights that the BMs of the top-performing companies are characterised by some common elements. From a practical perspective, the authors provide insights that can be useful for designing successful service-based BMs for service networks.

The purpose of this paper is to explore the key features of fashion rental from a business model perspective with a focus on the role played by digital platforms.

A qualitative research design was used based on multiple case studies of three Italian fashion rental enterprises.

The findings reveal the key aspects characterizing fashion rental business models and the centrality of digital platforms in value creation, configuration and capture activities. The study also found that fashion rental platforms are likely to exhibit further successful development in the future.

The academic literature on fashion rental has been mainly focussed on examining consumers' motivations and concerns, paying little attention to the enterprise's viewpoint. To date, no previous study has examined fashion rental business models from the retailer's perspective. This paper is the first to explore the key components of fashion rental business models and how digital platforms influence them from the perspective of retailers. The originality of the study is further strengthened by the unique context of analysis, namely, Italy, a leading country for global fashion.

This study aims to analyze the role of corporate social responsibility (CSR) customer perceptions, customer–company identification and customer trust on customer engagement (CE), paying special attention to the moderating effects of two types of social media communication, firm-generated content and user-generated content.

The study uses a mixed-methods’ approach. First, a single-factor experiment using Twitter posts as stimuli with 227 hotel guests. The structural model was analyzed using SmartPLS 3.2.7. Second, structured in-depth interviews were undertaken with three hotel industry experts to complement the conclusions of the quantitative study.

The results show that when a customer trusts a hotel and identifies with its corporate values, CSR tweets generate CE toward the hotel. CSR communications made by customers reinforce the impact of CSR tweets on customer trust more than CSR tweets posted by hotels. Hotel industry experts give insights to explain these results in different types of hotels.

CSR communications made through Twitter affect customers’ perceptions of a hotel’s CSR activities and customer trust in hotels, especially if they originate from a source external to the company. This result can be of use for hotel managers who have not previously given importance to active CSR communications or the interactivity of social media.

The authors show the moderating effect of user-generated content in the relationship between CSR customer perceptions and customer trust, thus contributing to the research into the effectiveness of social media. They use a mixed-methods’ approach to increase the validity of the results.

Este estudio analiza el papel de las percepciones de RSC, la identificación cliente-empresa (CCI) y la confianza en el engagement del cliente (CE), prestando especial atención al efecto moderador de dos tipos de comunicación en redes sociales: Contenido Generado por la Empresa (FGC) y Contenido Generado por el Usuario (UGC).

Se emplean métodos mixtos: un experimento de un factor usando publicaciones de Twitter como estímulo con 227 huéspedes del hotel, cuyo modelo estructural se analizó con SmartPLS 3.2.7., y 3 entrevistas en profundidad con expertos de la industria hotelera.

Los resultados muestran que, cuando un cliente confía en el hotel y se identifica con sus valores corporativos, los tweets de RSC generan engagement. La comunicación de RSC emitida por usuarios refuerza el impacto de los tweets de RSC en la confianza del cliente más que los tweets publicados por hoteles. Los expertos de la industria hotelera aportan nociones para explicar estos resultados en diferentes tipos de hoteles.

La comunicación de RSC realizada a través de Twitter afecta las percepciones del cliente sobre las actividades de RSC del hotel y la confianza en el mismo, especialmente si proceden de una fuente externa a la empresa. Este resultado puede ser útil para gerentes de hoteles que no se benefician de la comunicación activa de RSC o la interactividad de las redes sociales.

Se valida el efecto moderador del UGC en la relación entre las percepciones de RSC y la confianza de los clientes, contribuyendo así a la investigación sobre la efectividad de las redes sociales. Se emplea un diseño mixto para incrementar la validez de los resultados.

Comunicación de RSC, Engagement del Consumidor, Fuente del mensaje, Contenido generado por la empresa, Contenido generado por el usuario, Twitter, Compromiso con el cliente

Trabajo de investigación

The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation.

The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation.

Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent.

The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics.

This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency.

The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping.

Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research.

Failed deliveries (i.e. deliveries not accomplished due to the absence of customers) represent a critical issue in B2C (Business-to-consumer) e-commerce last-mile deliveries, implying high costs for e-commerce players and negatively affecting customer satisfaction. A promising option to reduce them would be scheduling deliveries based on the probability to find customers at home. This work proposes a solution based on presence data (gathered through Internet of Things [IoT] devices) to organise the delivery tours, which aims to both minimise the travelled distance and maximise the probability to find customers at home.

The adopted methodology is a multi-method approach, based on interviews with practitioners. A model is developed and applied to Milan (Italy) to compare the performance of the proposed innovative solution with traditional home deliveries (both in terms of cost and delivery success rate).

The proposed solution implies a significant reduction of missed deliveries if compared to the traditional operating mode. Accordingly, even if allocating the customers to time windows based on their availability profiles (APs) entails an increase in the total travel time, the average delivery cost per parcel decreases.

On the academic side, this work proposes and evaluates an innovative last-mile delivery (LMD) solution that exploits new AI (Artificial Intelligence)-based technological trends. On the managerial side, it proposes an efficient and effective novel option for scheduling last-mile deliveries based on the use of smart home devices, which has a significant impact in reducing costs and increasing the service level.

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 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.