Examining the Effect of Self-selection Bias on Consumer Satisfaction: A Product Type Perspective

Prior studies have evidenced that consumers can self-select themselves in submitting online reviews, thus introducing biases in the distribution of online review ratings. This kind of bias is termed self-selection bias. This research aims to explore the specific influences of self-selection bias on consumer satisfaction from a product type perspective. The adopted product classification system combines search and experience differentiation, as well as vertical and horizontal differentiation. An agent-based modeling approach is employed to systematically examine the combined effects of different types of self-selection bias and products. Based on experiment analysis, a novel theory is developed arguing that self-selection bias can have nuanced influences on consumer satisfaction with different kinds of products, by affecting the usefulness of online reviews in suggesting product quality information

From Block to TOE: Analyzing Opportunities of Blockchain Technologies in the Automotive Industry

Through the lens of the technology-organization-environment framework, this study aims to identify the relevant influencing factors and future opportunities for blockchain technology (BCT) adoption in the automotive industry. By applying an exploratory qualitative empirical study with semi-structured interviews with blockchain experts from the German automotive industry, a revised TOE framework is proposed in this context, confirming previous findings while also incorporating the newly discovered contextual factors of education & skills and sustainability. The analysis of a subsequent quantitative study reveals that while all factors affect BCT adoption, not all context factors have an equally strong impact. The most emphasized emerging BCT opportunities are autonomous driving, decentralized network, digital identity management, and traceability of the supply chain. The findings of this study provide guidance to organizations, politicians, consultants, and managers for defining strategies that aid in the successful adoption and value creation of BCT applications

The Digital Divide: Current and Future Research Directions

The digital divide refers to the separation between those who have access to digital information and communications technology (ICT) and those who do not. Many believe that universal access to ICT would bring about a global community of interaction, commerce, and learning resulting in higher standards of living and improved social welfare. However, the digital divide threatens this outcome, leading many public policy makers to debate the best way to bridge the divide. Much of the research on the digital divide focuses on first order effects regarding who has access to the technology, but some work addresses the second order effects of inequality in the ability to use the technology among those who do have access. In this paper, we examine both first and second order effects of the digital divide at three levels of analysis ? the individual level, the organizational level, and the global level. At each level, we survey the existing research noting the theoretical perspective taken in the work, the research methodology employed, and the key results that were obtained. We then suggest a series of research questions at each level of analysis to guide researchers seeking to further examine the digital divide and how it impacts citizens, managers, and economies

Factors in ICT Innovation's diffusion from an environmental context perspective: The case of XBRL

Objectives of the Study: The main objective of this study is to uncover which are the key dimensions that accelerate or retard the diffusion of XBRL within the context of an organization’s environment. There has been a lack of an environmental perspective on the diffusion research in previous studies on XBRL. As the globally coordinated standard of XML specifically developed for financial information reporting, the diffusion of XBRL is in a sense the study of the diffusion of digital online financial information. Academic background and methodology: The academic background of thesis includes the theories of the diffusion of innovations, standards, and models for external pressures impacting innovation adoption. The main theoretical model used for the empirical analysis is the Technology-Organization-Environment framework. The focus centred on the variables in the environmental context of organizations and this was divided into the four parts: technology support infrastructure, government regulation, industry characteristics and market structure, and cultural and other institutional pressures. The methodology implemented in the study included a case-study approach of target country environments with the data collected from semi-structured interviews. Findings and conclusions: The findings of the study showed that the most important factors found affecting the diffusion of XBRL are regulatory pressures. Additionally, to a lesser extent the occurrence of path dependencies, certain national market characteristics, and the support infrastructure have an impact

Value co-creation through APIs in Multi-sided platforms : A design science research in the E-Mobility industry

This thesis discusses various elements of value co-creation through APIs in the context of Multi-sided platforms. A design science research methodology is applied to answer the main research question of how APIs contribute to value co-creation in a Multi-sided platform environment. During the research, a model is developed that shows the impact of offering APIs to complementors, competitors and individual developers. This model is applied in the context of the E-Mobility industry. The target company, a Multi-sided platform provider that connects EV drivers with charging stations, serves as a real-world context for this thesis. During the application of the model, several artifacts are created, and the theoretical model is refined through direct feedback from business and IT professionals in the E-Mobility field

SUPPLY CHAIN RISK MANAGEMENT IN AUTOMOTIVE INDUSTRY

The automotive industry is one of the world\u27s most important economic sectors in terms of revenue and employment. The automotive supply chain is complex owing to the large number of parts in an automobile, the multiple layers of suppliers to supply those parts, and the coordination of materials, information, and financial flows across the supply chain. Many uncertainties and different natural and man-made disasters have repeatedly stricken and disrupted automotive manufacturers and their supply chains. Managing supply chain risk in a complex environment is always a challenge for the automotive industry. This research first provides a comprehensive literature review of the existing research work on the supply chain risk identification and management, considering, but not limited to, the characteristics of the automotive supply chain, since the literature focusing on automotive supply chain risk management (ASCRM) is limited. The review provides a summary and a classification for the underlying supply chain risk resources in the automotive industry; and state-of-the-art research in the area is discussed, with an emphasis on the quantitative methods and mathematical models currently used. The future research topics in ASCRM are identified. Then two mathematical models are developed in this research, concentrating on supply chain risk management in the automotive industry. The first model is for optimizing manufacturer cooperation in supply chains. OEMs often invest a large amount of money in supplier development to improve suppliers’ capabilities and performance. Allocating the investment optimally among multiple suppliers to minimize risks while maintaining an acceptable level of return becomes a critical issue for manufacturers. This research develops a new non-linear investment return mathematical model for supplier development, which is more applicable in reality. The solutions of this new model can assist supply chain management in deciding investment at different levels in addition to making “yes or no” decisions. The new model is validated and verified using numerical examples. The second model is the optimal contract for new product development with the risk consideration in the automotive industry. More specifically, we investigated how to decide the supplier’s capacity and the manufacturer’s order in the supply contract in order to reduce the risks and maximize their profits when the demand of the new product is highly uncertain. Based on the newsvendor model and Stackelberg game theory, a single period two-stage supply chain model for a product development contract, consisting of a supplier and a manufacturer, is developed. A practical back induction algorithm is conducted to get subgame perfect optimal solutions for the contract model. Extensive model analyses are accomplished for various situations with theoretical results leading to conditions of solution optimality. The model is then applied to a uniform distribution for uncertain demands. Based on a real automotive supply chain case, the numerical experiments and sensitivity analyses are conducted to study the behavior and performance of the proposed model, from which some interesting managerial insights were provided. The proposed solutions provide an effective tool for making the supplier-manufacturer contracts when manufacturers face high uncertain demand. We believe that the quantitative models and solutions studied in this research have great potentials to be applied in automotive and other industries in developing the efficient supply chains involving advanced and emerging technologies