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Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time
(2021)
Discrete choice experiments have emerged as the state-of-the-art method for measuring preferences, but they are mostly used in cross-sectional studies. In seeking to make them applicable for longitudinal studies, our study addresses two common challenges: working with different respondents and handling altering attributes. We propose a sample-based longitudinal discrete choice experiment in combination with a covariate-extended hierarchical Bayes logit estimator that allows one to test the statistical significance of changes. We showcase this method’s use in studies about preferences for electric vehicles over six years and empirically observe that preferences develop in an unpredictable, non-monotonous way. We also find that inspecting only the absolute differences in preferences between samples may result in misleading inferences. Moreover, surveying a new sample produced similar results as asking the same sample of respondents over time. Finally, we experimentally test how adding or removing an attribute affects preferences for the other attributes.
This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
The mobile games business is an ever-increasing sub-sector of the entertainment industry. Due to its high profitability but also high risk and competitive atmosphere, game publishers need to develop strategies that allow them to release new products at a high rate, but without compromising the already short lifespan of the firms' existing games. Successful game publishers must enlarge their user base by continually releasing new and entertaining games, while simultaneously motivating the current user base of existing games to remain active for more extended periods. Since the core-component reuse strategy has proven successful in other software products, this study investigates the advantages and drawbacks of this strategy in mobile games. Drawing on the widely accepted Product Life Cycle concept, the study investigates whether the introduction of a new mobile game built with core-components of an existing mobile game curtails the incumbent's product life cycle. Based on real and granular data on the gaming activity of a popular mobile game, the authors find that by promoting multi-homing (i.e., by smartly interlinking the incumbent and new product with each other so that users start consuming both games in parallel), the core-component reuse strategy can prolong the lifespan of the incumbent game.
Device-to-device (D2D) communication is an innovative solution for improving wireless network performance to efficiently handle the ever-increasing mobile data traffic. Communication takes place directly between two devices that are in each other’s transmission range. So far, research has focused on the technical challenges of implementing this technology and assumes a user’s general willingness to participate as forwarder in this technology. However, this simplifying assumption is not realistic, as willingness to participate in D2D communication can vary depending on the user. In this work, we consider the scenario that a user can act as a forwarder for a receiver who is not directly or insufficiently reached by the base station and accordingly has no or poor Internet connection. We take a user-centric approach and investigate the willingness to provide an Internet connection as a forwarder. We are the first to investigate user preferences for D2D communication using a choice-based conjoint analysis. Our results, based on a representative sample of potential users (N=181), show that the social relationship between the potential forwarder and the receiver has the greatest impact on the potential forwarder’s decision to provide an Internet connection to the receiver, accepting sacrifices in terms of additional battery consumption and reduced own service performance. In a detailed segment analysis, we observe significant preference differences depending on smartphone usage behavior and user age. Taking the corresponding preferences into account when matching forwarders and receivers can further increase technology adoption.
In the upcoming years, the internet of things (IoT)will enrich daily life. The combination of artificial intelligence(AI) and highly interoperable systems will bring context-sensitive multi-domain services to reality. This paper describesa concept for an AI-based smart living platform with open-HAB, a smart home middleware, and Web of Things (WoT) askey components of our approach. The platform concept con-siders different stakeholders, i.e. the housing industry, serviceproviders, and tenants. These activities are part of the Fore-Sight project, an AI-driven, context-sensitive smart living plat-form.
The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.
Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.
Having a gatekeeper position in a collaborative network offers firms great potential to gain competitive advantages. However, it is not well understood what kind of collaborations are associated with such a position. Conceptually grounded in social network theory, this study draws on the resource-based view and the relational factors view to investigate which types of collaboration characterize firms that are in a gatekeeper position, which ultimately could improve firm performance in subsequent periods. The empirical analysis utilizes a unique longitudinal data set to examine dynamic network formation. We used a data crawling approach to reconstruct collaboration networks among the 500 largest companies in Germany over nine years and matched these networks with performance data. The results indicate that firms in gatekeeper positions often engage in medium-intensity collaborations and less likely weak-intensity collaborations. Strong-intensity collaborations are not related to the likelihood of being a gatekeeper. Our study further reveals that a firm's knowledge base is an important moderator and that this knowledge base can increase the benefits of having a gatekeeper position in terms of firm performance.
The present study investigates the moderating effect of usage intensity of the social networking site (SNS) Instagram (IG) on the influence of advertisement disclosure types on advertising performance. A national sample (N = 566) participated in a randomized online experiment including a real influencer and followers in order to investigate how different advertisement disclosure types affect advertising performance and how usage intensity moderates this effect. We find that disclosing an influencer’s postings with “#ad” increases the trustworthiness of the influencer and the general credibility of the posting for heavy users, but not for light users. Followership of a user has been found to strongly improve all researched variables (attitude toward product placement, trustworthiness of the spokesperson and general credibility of the posting). This study adds to literature the first distinction on heavy and light usage intensity, and on followership of an IG user when regarding the effects of advertisement disclosure types on advertising performance. To conclude, we present a number of recommendations regarding how advertisers, influencers, and SNS providers should develop strategies for monitoring, understanding, and responding to different social media users, e.g., to closely monitor an influencer’s audience to identify heavy users and optimally target them.
Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant to adopt these sophisticated data analytics methods as research instruments. Even the Information Systems (IS) discipline with its close connections to Computer Science seems to be conservative when conducting empirical research endeavors. To assess the magnitude of the problem and to understand its causes, we conducted a bibliographic review on publications in high-level IS journals. We reviewed 1,838 articles that matched corresponding keyword-queries in journals from the AIS senior scholar basket, Electronic Markets and Decision Support Systems (Ranked B). In addition, we conducted a survey among IS researchers (N = 110). Based on the findings from our sample we evaluate different potential causes that could explain why ML methods are rather underrepresented in top-tier journals and discuss how the IS discipline could successfully incorporate ML methods in research undertakings.