Veröffentlichungen von Lea Reis (geb. Lea Müller)

Mixed-methods studies are on the rise in information systems (IS) research, as they deliver robust and insightful inferences combining qualitative and quantitative research. However, there is much divergence in conducting such studies and reporting their findings. Therefore, we aim (1) to evaluate how mixed-methods studies have developed in information systems (IS) research under the existence of heavily used guidelines presented by Venkatesh et al. (2013) and (2) to reflect on those observations in terms of potentials for future research. During our review, we identified 52 mixed-methods papers and quantitatively elaborated the adherence to the three core concepts of mixed-methods in terms of purpose, meta-inferences, and validation. Findings discover that only eight adhere to all three of them. We discuss the significance of our results for current and upcoming mixed-methods research and derive specific suggestions for authors. With our study, we contribute to mixed-methods research by showing how to leverage the insights from existing guidelines to strengthen future research and by contributing to the discussion of the legislation associated with research guidelines, in general, presenting the status quo in current literature

Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and platforms, (d) chatbots for collaboration, (e) democratizing chatbots, and (f) ethics and privacy. For each of these topics, we provide a brief overview of the state of the art, discuss key research challenges, and suggest promising directions for future research. The six topics are detailed with a 5-year perspective in mind and are to be considered items of an interdisciplinary research agenda produced collaboratively by avid researchers in the field.

Individuals only click on a very small fraction of the in-app advertisements (ads) they are exposed to. Despite this fact, organizations spend generously placing in-app ads without theoretical knowledge of how the structure and the semantics of in-app ads influence individuals’ clicking behavior. This study aims to identify how the processing of structural and semantic factors leads to clicking behavior. Based on the limited capacity theory, this paper proposes that the sequential processing of structural and semantic factors leads to clicking behavior. To mirror the sequential process, this paper applies a process-oriented configurational approach and performs a two-step qualitative comparative analysis (QCA) using 262 incidents of exposure to in-app ads. The results support the proposed sequential processing and show that neither structural nor semantic factors alone lead to clicking behavior. This paper reveals four different paths of sequential processing of in-app ads that lead to clicking behavior. The results show that individuals click on non-animated in-app ads even though these are perceived as irritating or privacy-concerning. When the in-app ads are animated, individuals do only click on them when these are not irritating, privacy-concerning and personalized. Organizations can use these findings to improve their in-app ads and generate more clicks. This study recommends that organizations place in-app ads in a prominent location, design them similar to the design of the app and use bright colors. The advertising message needs to have new and relevant information in a credible and entertaining way. Depending on the degree of personalization, organizations should use different sizes of the in-app ad and only use animation if it is unlikely that the in-app ad caused irritation or privacy concerns. Organizations can use these findings to improve their in-app ads and generate more clicks. This paper recommends that organizations place in-app ads in a prominent location, design them similar to the design of the app and with bright colors. The advertising message needs to have new and relevant information in a credible and entertaining way. Depending on the degree of personalization, organizations should use different sizes of the in-app ad and only use animation if it is unlikely that the in-app ad caused irritation or privacy concerns. From the in-app ad perspective, this study is the first to theoretically develop and empirically show the sequential processing of structural and semantic factors of in-app ads. From the methodological perspective, this study applies an advanced configurational two-step QCA approach, which is capable of analyzing sequential processes and is new to marketing research.

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.

Integrating artificial intelligence (AI) into existing work routines involves invasive changes, and the resulting user resistance can lead to project failure. We describe a failed AI project at a large hospital to implement a cognitive agent and identify the root causes of the user resistance that led to the failure. Based on the lessons learned, we provide recommendations for addressing the causes of resistance for the three types of AI—automation, decision support and engagement.

When do social media users click on sponsored content or intend to visit the website at a later time? A qualitative comparative analysis (QCA) using arguments based on herd theory, strength of ties, and social distance shows that only “likes” from socially close and knowledgeable users can consistently generate click-through or view-through intentions. Considering social tie strength in a herd behavior context, the analysis of sufficient configurations for click- and view-through intentions provides a nuanced perspective on social media user behavior and social influence. For instance, click-through intention requires observing a “like” from a close person, while view-through intentions can also develop after observing “likes” from less close acquaintances, yet in the last case only if the user assumes the acquaintance is better informed regarding the sponsored content. In addition, a “like” from a close friend deemed better informed can even make a user click on a sponsored content that was not considered valuable before.

Bitcoin is a well-established blockchain-based cryptocurrency that has attracted a great deal of attention from media and regulators alike. While millions of individuals invest in bitcoin, their motivations for doing so are less clear than with traditional investment decisions. We argue that the technical nature of bitcoin investments gives it unique characteristics and, consequently, that we lack a thorough understanding of how this affects the motivations behind bitcoin investment. We use a mixed method approach consisting of qualitative (n = 73) and quantitative (n = 150) studies and fuzzy-set qualitative comparative analysis (fsQCA) to identify seven bitcoin-specific motivations (profit expectancy, ease of bitcoin acquisition, support of bitcoin ideology, investment skills, risk affinity, anticipated and experienced inaction regret) and how configurations of them explain bitcoin investment. The findings reveal, among others, that some individuals invest in bitcoin because they support the bitcoin ideology. Contrary to the traditional investment literature, profit expectancy is not a necessary condition to the extent that there is one empirical configuration of motivations that explains that individuals also invest in bitcoin even if they do not expect profits. The results disclose non-trivial investment motivation configurations and lay the groundwork for future studies of the role of cryptocurrencies in society.