Will They Die Another Day? A Decision Support Perspective on Reusing Electric Vehicle Batteries

The diffusion of electric mobility suffers from an immature and expensive battery technology. Reusing electric vehicle batteries (EVBs) is a prospective opportunity for lowering the total costs of ownership of electric vehicles and using scarce natural resources more efficiently. However, to determine how to reuse a battery is a complex decision problem. In this study we set out to develop a design theory for a class of decision support systems (DSSs) that implement two main functions: First, a consideration set of feasible reuse scenarios is compiled based on an assess-ment of a battery’s structure and condition. Second, an offering is configured based on bun-dling batteries with customized services. We conclude with an outlook to our ongoing design science project that will, amongst others, explore to what extent systems instantiated from the design theory can remedy adverse effects caused by the ‘lemon market’ properties of the sec-ond-hand battery market

Online Regularization for High-Dimensional Dynamic Pricing Algorithms

We propose a novel \textit{online regularization} scheme for revenue-maximization in high-dimensional dynamic pricing algorithms. The online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (\texttt{OORMLP}) algorithm with three major advantages: encode market noise knowledge into pricing process optimism; empower online statistical learning with always-validity over all decision points; envelop prediction error process with time-uniform non-asymptotic oracle inequalities. This type of non-asymptotic inference results allows us to design safer and more robust dynamic pricing algorithms in practice. In theory, the proposed \texttt{OORMLP} algorithm exploits the sparsity structure of high-dimensional models and obtains a logarithmic regret in a decision horizon. These theoretical advances are made possible by proposing an optimistic online LASSO procedure that resolves dynamic pricing problems at the \textit{process} level, based on a novel use of non-asymptotic martingale concentration. In experiments, we evaluate \texttt{OORMLP} in different synthetic pricing problem settings and observe that \texttt{OORMLP} performs better than \texttt{RMLP} proposed in \cite{javanmard2019dynamic}

AI-Based Innovation in B2B Marketing: An Interdisciplinary Framework Incorporating Academic and Practitioner Perspectives

Artificial intelligence (AI) rests at the frontier of technology, service, and industry. AI research is helping to reconfigure innovative businesses in the consumer marketplace. This paper addresses existing literature on AI and presents an emergent B2B marketing framework for AI innovation as a cycle of the critical elements identified in cross-functional studies that represent both academic and practitioner strategic orientations. We contextualize the prevalence of AI-based innovation themes by utilizing bibliometric and semantic content analysis methods across two studies and drawing data from two distinct sources, academics, and industry practitioners. Our findings reveal four key analytical components: (1) IT tools and resource environment, (2) innovative actors and agents, (3) marketing knowledge and innovation, and (4) communications and exchange relationships. The academic literature and industry material analyzed in our studies imply that as markets integrate AI technology into their offerings and services, a governing opportunity to better foster and encourage mutually beneficial co-creation in the AI innovation process emerges

A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques

Cloud computing as a fairly new commercial paradigm, widely investigated by different researchers, already has a great range of challenges. Pricing is a major problem in Cloud computing marketplace; as providers are competing to attract more customers without knowing the pricing policies of each other. To overcome this lack of knowledge, we model their competition by an incomplete-information game. Considering the issue, this work proposes a pricing policy related to the regret minimization algorithm and applies it to the considered incomplete-information game. Based on the competition based marketplace of the Cloud, providers update the distribution of their strategies using the experienced regret. The idea of iteratively applying the algorithm for updating probabilities of strategies causes the regret get minimized faster. The experimental results show much more increase in profits of the providers in comparison with other pricing policies. Besides, the efficiency of a variety of regret minimization techniques in a simulated marketplace of Cloud are discussed which have not been observed in the studied literature. Moreover, return on investment of providers in considered organizations is studied and promising results appeared

Mapping the Emerging Field of Service Science: Insights from a Citation Network and Cocitation Network Analysis

The purpose of this study is to comprehensively map the recent additions to the body of knowledge in the service science discipline. Previous literature analyses insufficiently account for these developments or refrain from applying tool-based bibliographic analysis techniques. Following the introduction of the software tool CiteBridge, a citation network and a cocitation network are constructed based on 3,783 articles and 6,775 citations. Subsequently, both networks are analyzed (a) to map the scope and structure of the discipline, (b) to identify the most authoritative papers and literature review papers, (c) to discover clusters of research in the discipline, and (d) to explore if the service dominant logic of marketing has evolved into an overarching philosophical foundation for service research. The findings are intended to provide researchers with a sound orientation about the recent developments in the field and to further shape the evolution of service science as a research discipline

Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions

Purpose-Although the value of AI has been acknowledged by companies, the literature shows challenges concerning AI-enabled B2B marketing innovation, as well as the diversity of roles AI can play in this regard. Accordingly, this study investigates the approaches that AI can be used for enabling B2B marketing innovation. Design/methodology/approach-Applying a bibliometric research method, this study systematically investigates the literature regarding AI-enabled B2B marketing. It synthesises state-of-the-art knowledge from 221 journal articles published between 1990 and 2021. Findings-Apart from offering specific information regarding the most influential authors and most frequently cited articles, the study further categorises the use of AI for innovation in B2B marketing into five domains, identified the main trends in the literature, and suggest directions for future research. Practical implications-Through our identified five domains, practitioners can assess their current use of AI ability in terms of their conceptualisation capability, technological applications, and identify their future needs in the relevant domains in order to make appropriate decisions on whether to invest in AI. Thus, the research outcomes can help companies to realise their digital marketing innovation strategy through AI. Originality/value-While more and more studies acknowledge the potential value of AI in B2B marketing, few attempts have been made to synthesise the literature. The results from the study can contribute by 1) obtaining and comparing the most influential works based on a series of analyses; 2) identifying five domains of research into how AI can be used for facilitating B2B marketing innovation; and 3) classifying relevant articles into five different time periods in order to identify both past trends and future directions in this specific field

Electronic word of mouth in online social networks: strategies for coping with opportunities and challenges

In today's world, the widespread success of the Internet, social media, and online social networks (OSN) provide the basis for electronic word of mouth (EWOM). EWOM can be seen as a digital enhancement of traditional word of mouth that makes communication more efficient and involves less effort by its users. The resulting speed of diffusion and information transparency have caused transformative changes in consumer behaviour in all types of markets, which requires the development of new business strategies for adequately dealing with the new circumstances. This doctoral dissertation is divided into three overall subject areas that concern the investigation of capable strategies for coping with the emerged opportunities and challenges of EWOM in OSN. The first subject area concerns negative electronic word of mouth in OSN and investigates capable countermeasure strategies for firms to adequately address claims of unsatisfied customers. For this, three simulation studies are conducted in which the propagation of a negative message and its countering by a positive message published by the firm are numerically analysed. The results reveal that, in general, the persuasiveness of a firm's response is more important than a quick response with a less persuasive counter-message. To some extent, this also holds if the number of OSN members who initially disseminate the counter-message on behalf of the firm is increased. In the second subject area, an optimisation model for individualised pricing is developed for an online store whose customers are interconnected in an OSN and can share price information via EWOM. The model is solved numerically by artificial intelligence solution methods. The results indicate that personalised prices can be financially worthwhile even under price transparency. The third subject area investigates market entry strategies for social media apps and services that are advertised in an OSN for acquiring new users and examines the role of EWOM in this context. A diffusion model is developed and analysed numerically by simulation. Three different targeting approaches are compared to each other regarding their ability to reach a high share of active users in the OSN: (1) a random marketing strategy, where randomly chosen members in the OSN are presented the advertisement, (2) cluster marketing, where whole clusters of members who are densely connected to each other are simultaneously shown the advertisement, and (3) influencer marketing, where the most influential users in the OSN are selected to share sponsored posts about the app in the OSN. The results suggest that EWOM can have detrimental effects if OSN members are too early informed about the app or service. If the information about the app reaches clusters in the OSN prematurely where a sufficient level of activity is not present yet, it can deplete the excitement of the users. The lack of excitement, in turn, can significantly reduce the effect of subsequent marketing campaigns. However, if applied appropriately, a higher level of EWOM about the app or service can increase the performance of the random marketing strategy to the extent that it outperforms cluster and influencer marketing