Charged Less, Paid More - Non-optimal Tariff Choice Decisions in the Electric Vehicle Services Market

Electric vehicle users need to subscribe to an Electric Mobility Platform Service provider to gain access to public charging networks. Consequently, consumers need to form beliefs about their future demand for the (charging) service in order to choose a tariff that maximizes their surplus. Using a unique dataset from a large Western European Electric Mobility Platform Service provider, we show that a significant share of customers conducts systematic tariff-choice errors. We find that customers of two-part tariffs are more likely to choose a non-optimal tariff than customers of a pay-per-use tariff. Additionally, the likelihood of a non-optimal pay-per-use tariff choice depends on the user’s type of plug-in electric vehicle. We explain the non-optimal tariff choices by cognitive biases related to reference dependence and overconfidence. We further outline our next steps to better understand non-optimal choice behavior in the electric vehicle services market and provide implications for managers and policymakers

KNOWLEDGE STOCK EXCHANGES: A CO-OPETITIVE CROWDSOURCING MECHANISM FOR E-LEARNING

Modern information and communication technologies (ICT) provide numerous opportunities to support e-learning in higher education. Recent devlopments such as Massive Open Online Courses (MOOCs) utilize the scalabiltiy and interactivity of the ICT to broaden the accessibility of university education. However, the potential of ICT in enhancing students´ learning experience and success is far from being fully utilized. One potential area for the development of new e-learning mechanisms is at the intersection of collective intelligence and crowdsourcing mechanisms: The knowledge-disseminating ability of a collective intelligence platform combined with the interactivity and participative nature of crowdsourcing knowledge from fellow students may enhance motiviation and acceptance of students´ learning. Following a crowd-based approach we present a prototype that offers a highly collaborative and competitive learning environment to improve the mutual exchange of knowledge as well as to encourage the development of a knowledge community. Our approach draws upon the principle of virtual stock markets (also prediction markets ), a well-known collective intelligence mechanism which we enhanced with crowdsourcing elements. We describe the proposed system architecture, evaluate the practical feasibility of our prototype in the field and provide implications for future research

Pay today, or delay the pay: Consumer preference for double flat-rate pricing plans

Double flat-rate pricing plans are a pricing strategy used in a variety of industries, including digital add-on services for durable products. These pricing plans consist of two distinct components: a nonrecurring flat rate and a recurring flat rate. A nonrecurring flat rate consists of a one-time, initial, nonrecurring provisioning fee. A recurring flat rate is a recurring (usually monthly) subscription fee that entitles consumers to unlimited access to the service without additional usage-based charges. While previous research has extensively studied single flat-rate pricing plans, consumer preference for double flat-rate pricing plans compared to single flat-rate plans has not yet been studied. We conduct two discrete choice experiments for utilitarian products in different industries and find that—contrary to the increasing use of double flat-rate pricing plans—consumers tend to prefer single flat-rate plans. Moreover, we find substantial preference heterogeneity for the two pricing plan components. Nonrecurring flat-rate fees have a greater influence on consumer choice than recurring flat-rate fees. We discuss the theoretical implications for behavioral pricing and consumers’ tariff choice decisions, as well as the managerial implications for firms’ pricing menu decisions

Influence of Assimilation Effects on Recommender Systems

Recommender systems are a common approach in retail e-commerce to support consumers in finding relevant products. Not surprisingly, user acceptance of personalized product recommendations tends to be higher, leading to higher click rates. Since contextual information also influences user search behavior, we analyze the importance of similarity between recommendations and the underlying context a currently inspected product provides. Using data from a midsize European retail company, we conduct a field experiment and investigate the role of similarities between focal product information and recommendations from a collaborative filtering algorithm. We find that contextual similarity, primarily visual similarity contributes much explanation to consumer click behavior, underlining the importance of contextual and content information in the recommender system\u27s environment

The Impact of Uncertainty on Customer Satisfaction

Customer satisfaction is an important metric to predict customer behavior and as a result firms' profitability. Expectations of a product's performance serve as a reference point against which customers evaluate their satisfaction with the products' actual performance. However, what is the effect of uncertainty in expectations? This paper develops a novel theoretical model of satisfaction, in which expectations reflect distributions of individual beliefs about performance outcomes. Based on this model, uncertainty shifts subjective reference points upward. That is, uncertainty increases the performance level at which customers switch from being dissatisfied to being satisfied. Furthermore, uncertainty has an attenuating effect on both positive and negative deviations of actual performance from subjective reference points. Put differently, a bad performance feels less bad and a good performance feels less good when it is expected, compared with unexpected. The authors find support for the model's predictions in an experimental study on product delivery as well as a field study based on online reviews. In addition, the authors develop a model-based tool that predicts the effect of uncertainty on customer satisfaction across different customizable scenarios. The paper's results carry implications for firms' communication, customer valuation and recovery strategies

Measuring Frictional Costs in E-Commerce: The Case of Name-Your-Own-Price Auctions

Frictional costs are defined as the disutility related to the conduct of an online transaction. Thus,frictional costs can accrue through the consumer‘s decision-making process prior to an onlinetransaction, e.g., bidding in interactive pricing mechanisms like auctions. We present two models forthe measurement of frictional costs in Name-Your-Own-Price auctions where these costs can either bemeasured through a discount factor or in absolute values. We compare the fit and estimation results ofthese models by analyzing bidding data from a German NYOP seller. Our results show that bothmodels are equally parsimonious, explain a comparable fraction of variance and both models yieldrobust and reasonable parameter estimates

New Product Development with Internet Based Information Markets: Theory and Empirical Application

Successful new product development is crucial for firms’ competitive advantage. Despite various sophisticated methods and high investments, new products still face notoriously high failure rates. A very critical stage in new product development is product concept testing for the go/no-go decision in further product development. Since there is a high number of different product concepts to test, there obviously is a need for a reliable, valid and efficient method, which can benefit from the scalability and interactivity of Internet-based technologies. Internet-based information markets are a new method to support new product development, based on the market efficiency hypothesis. We empirically evaluate product concepts with information markets. Further, we compare the results of the information markets with traditional research methods

When reality backfires: Product evaluation context and the effectiveness of augmented reality in e‐commerce

Augmented reality (AR) enables consumers to project product holograms into their surrounding real-world context in real time using their mobile devices. Although AR may improve online consumers' product evaluation, AR-deploying retailers give up control over the context in which their products are evaluated. As a result, AR retailers' products might end up being evaluated in unfavorable contexts, such as disorganized rooms. Negative spillover effects from such unfavorable AR contexts onto the perceptions of evaluated products may lead consumers to refrain from purchasing the products. In two online experiments and a controlled field study with a total of 1000 participants, we find that unfavorable AR contexts negatively affect consumers' product-related purchase intention. This relationship is serially mediated by processing disfluency and deteriorating product quality perceptions of consumers. The negative contextual effects are mitigated if the product under evaluation is of unique design and thus more conceptually fluent or if the AR context becomes less perceptually salient and thus the product more perceptually fluent. We discuss diminished reality and facilitated product comparisons via AR as potential countermeasures for AR retailers and provide suggestions for future research

The value of distinctiveness: Product uniqueness in crypto marketing

Marketers across industries appeal to consumers’ need for uniqueness in their marketing and product strategies. While there is an understanding of the many benefits of such a strategy and its underlying mechanisms, the effects are often linked to product scarcity, leaving a product’s distinctiveness compared to similar products unexplored. In this study, we examine the effect of product attribute distinctiveness using transaction data of a large non-fungible token (NFT) collection. Despite identical initial launch prices for all products in the collection, secondary sale prices vary substantially. Using a selection model, our results show that a unique product is less likely to be resold. We also find a positive relationship between attribute distinctiveness and transaction value. This indicates the importance of such product information to consumers. The implications of our empirical study add to the literature on uniqueness, NFTs, and crypto marketing