Exploring the Influence of Trust on Mobile Payment Adoption

The objective of this study is to explore antecedents of trust and the influence of trust on intention to use mobile payments. The research examines three dimensions of trust antecedents including trust perceptions of the mobile service provider, the mobile payment vendor and mobile technology. The results are based on a survey sample of 302 participants. PLS-SEM is employed in the data analysis. Results reveal that trust is a crucial factor of consumer’s intention to adopt mobile payment. Results highlight that characteristics of the mobile service provider, mobile payment vendor and mobile technology influence the development of trust on mobile payment. In particular, consumer’s perceptions of structural assurance and environmental risks of mobile technology have strong influence on mobile payment trust. Results also highlight that consumers’ perceived reputation of the mobile service provider and mobile payment vendor positively relate to mobile payment trust

Research in multi-cultural relationship building

This study aims to explore the ‘missing gap' between the values of an Accounting firm and the preference shown by Maaori on how they would like to be approached when wanting to build a trusted relationship within a business sense. This study makes use of qualitative approaches in which data is collected primarily through interviews and analysed to produce results and recommendations. The study found that Maaori would like to be approached in a way that makes sense to them and also identifies with their cultural proceedings. It also provides insight into how important trust is when establishing a relationship with Maaori. The study recommends that further studies conducted should interview a wider variety of focus groups to add different elements to this research and that FIRM A's small business department's offerings do not align with what Maaori want so need to be rethought to adapt to Maaor expectations

Analysis and optimization of distribution logistics for Just Water Company

This report details the various factors that affect the operational efficiency of distribution logistics. The research aimed at studying the existing components involved in the distribution logistics of Just Water. Distribution logistics begins at the end of the production line where the finished product is emerged until it reaches the customers. The literature review explains the key components of distribution logistics in an organisation. This research analyses the existing components of the distribution logistics of Just Water and discusses possible improvements that can be adapted to increase the overall efficiency of the distribution logistics operation. The background of the research is that Just Water faces difficulty with delivering its products on time during peak seasons. The research tries to unveil the reason for this delay and finds that the demands for extra water-out deliveries are interfering with the normal runs of the trucks, therein delaying their regular schedule. One another cause was found to be the shortage of supplies due to slow or less return logistics. The research suggests a change in the existing drop shipping distribution model and recommends the adaptation of intermediary or multi-stage distribution networks, possibly the ‘Last Mile Delivery’ configuration in order to reduce delivery lead-time, reduce transportation costs and improve customer satisfaction

Designing AI-Based Systems for Qualitative Data Collection and Analysis

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS. Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups. With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups

Managing the Paradox of Growth in Brand Communities Through Social Media

The commercial benefits of online brand communities are an important focus for marketers seeking deeper engagement with increasingly elusive consumers. Managing participation in these socially bound brand conversations challenges practitioners to balance authenticity towards the community against corporate goals. This is important as social media proliferation affords communities the capacity to reach a scale well beyond their offline equivalents and to operate independently of brands. While research has identified the important elements of engagement in brand communities, less is known about how strategies required to maximise relationships in these circumstances must change with growth. Using a case study approach, we examine how a rapidly growing firm and its community have managed the challenges of a maturing relationship. We find that, in time, the community becomes self-sustaining, and a new set of marketing management strategies is required to move engagement to the next level