Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system’s explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity.

<jats:title>Abstract</jats:title><jats:p>Business process management (BPM) drives corporate success through effective and efficient processes. In recent decades, knowledge has been accumulated regarding the identification, discovery, analysis, design, implementation, and monitoring of business processes. This includes methods and tools for tackling various kinds of process change such as continuous process improvement, process reengineering, process innovation, and process drift. However, exogenous shocks, which lead to unintentional and radical process change, have been neglected in BPM research although they severely affect an organization’s context, strategy, and business processes. This research note conceptualizes the interplay of exogenous shocks and BPM in terms of the effects that such shocks can have on organizations’ overall process performance over time. On this foundation, related challenges and opportunities for BPM via several rounds of idea generation and consolidation within a diverse team of BPM scholars are identified. The paper discusses findings in light of extant literature from BPM and related disciplines, as well as present avenues for future (BPM) research to invigorate the academic discourse on the topic.</jats:p>

<jats:title>Abstract </jats:title><jats:p>Technological developments such as Cloud Computing, the Internet of Things, Big Data and Artificial Intelligence continue to drive the digital transformation of business and society. With the advent of platform-based ecosystems and their potential to address complex challenges, there is a trend towards greater interconnectedness between different stakeholders to co-create services based on the provision and use of data. While previous research on digital transformation mainly focused on digital transformation <jats:italic>within</jats:italic> organizations, it is of growing importance to understand the implications for digital transformation on different layers (e.g., interorganizational cooperation and platform ecosystems). In particular, the conceptualization and implications of public data spaces and related ecosystems provide promising research opportunities. This special issue contains five papers on the topic of digital transformation and, with the editorial, further contributes by providing an initial conceptualization of public data spaces' potential to foster innovative progress and digital transformation from a management perspective.</jats:p>

<jats:title>Abstract</jats:title><jats:p>While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.</jats:p>

<jats:title>Abstract</jats:title><jats:p>While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.</jats:p>

Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.

<jats:title>Zusammenfassung</jats:title><jats:p>Durch technologische Fortschritte in den letzten Jahren ist Virtual Reality erschwinglicher und benutzerfreundlicher geworden, sodass Unternehmen die Einführung der Technologie verstärkt in Betracht ziehen. Ihren Aufschwung erlebte die Technologie jedoch durch die Unterhaltungs- und Spieleindustrie, weshalb sich für Unternehmen die Frage nach sinnvollen Anwendungsfällen stellt. Nach der Affordance-Experimentation-Actualization-Theorie ist insbesondere bei neu aufkommenden Technologien eine Experimentierphase notwendig, um Handlungsmöglichkeiten aufzudecken und daraus Anwendungsfälle zu generieren. Dieser Artikel präsentiert die Ergebnisse einer Fallstudie in einem Unternehmen, das sich während der Studie in der Experimentierphase befand. Durch Interviews mit acht Beschäftigten und einem Vertriebspartner konnten drei Handlungsmöglichkeiten für Virtual Reality im Unternehmenskontext und eine zuvor nicht bekannte Aktivität der Experimentierphase identifiziert werden. Damit erweitert die Studie bisherige Forschung zur Experimentierphase und zeigt Unterschiede im Vergleich zu anderen innovativen Technologien auf, die in vorherigen Studien untersucht wurden. Für Unternehmen bietet die Studie wertvolle Einblicke in die erfolgreiche Gestaltung der Experimentierphase als Vorbereitung auf die Implementierung.</jats:p>

The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns.</jats:p>

<jats:title>Zusammenfassung</jats:title><jats:p>Coworking Spaces (CSPs) sind geteilte Arbeitsplätze für Selbstständige, Freelancer*innen, Mikrounternehmen und Startups, die Isolation entgegenwirken und zum interdisziplinären Wissensaustausch anregen können. Jedoch existieren auch Barrieren, die Nutzer*innen davon abhalten, zu anderen Coworker*innen Kontakt aufzunehmen, da oft unklar ist, wann und ob jemand zum kreativen Austausch oder zum Anbieten von Hilfe bereit ist. Durch die Covid-19 Pandemie wurde die Unsicherheit bei der gegenseitigen Kontaktaufnahme noch weiter erschwert und viele CSPs mussten zeitweise schließen. Um Barrieren bei der Kontaktaufnahme zu reduzieren und die interdisziplinäre Zusammenarbeit zu fördern, können Informations- und Kommunikationstechnologien eingesetzt werden. Virtual Reality (VR) und Augmented Reality (AR) sind Technologien, die sich durch einen besonders hohen Grad an Immersion und sozialer Präsenz auszeichnen. Deshalb zeigen wir in diesem Beitrag, wie VR- und AR-Technologien gezielt eingesetzt werden können, um den interdisziplinären Wissensaustausch und Zusammenarbeit sowohl in CSPs als auch ortsunabhängig zu fördern. Dazu präsentieren wir positive Effekte, die durch den Einsatz einer der beiden Technologien im Zusammenhang mit CSPs erzielt werden können und leiten konkrete Gestaltungsempfehlungen für Anwendungsentwickler*innen, Unternehmen sowie Betreiber*innen von CSPs ab. Diese Gestaltungsempfehlungen basieren sowohl auf den neuesten Erkenntnissen aus der Fachliteratur als auch auf Interviews mit Expert*innen aus Forschung und Praxis mit Erfahrung im Bereich CSPs, VR und AR. Unsere Anwendungsszenarien können Entwickler*innen, Unternehmen und Betreiber*innen von CSPs als Grundlage dienen, vom Einsatz beider Technologien zu profitieren.</jats:p>

Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research has only recently developed automated citation classification techniques to distinguish between different types of citations and generally does not emphasize the conceptual content of the citations and its ideational impact. To address this problem, we develop Deep Content-enriched Ideational Impact Classification (Deep-CENIC) as the first automated approach for ideational impact classification to support researchers' literature search practices. We evaluate Deep-CENIC on 1,256 papers citing 24 information systems review articles from the IT business value domain. We show that Deep-CENIC significantly outperforms state-of-the-art benchmark models. We contribute to information systems research by operationalizing the concept of ideational impact, designing a recommender system for academic papers based on deep learning techniques, and empirically exploring the ideational impact of the IT business value domain.

<jats:title>Abstract</jats:title><jats:p>Organizations introduce virtual assistants (VAs) to support employees with work-related tasks. VAs can increase the success of teamwork and thus become an integral part of the daily work life. However, the effect of VAs on virtual teams remains unclear. While social identity theory describes the identification of employees with team members and the continued existence of a group identity, the concept of the extended self refers to the incorporation of possessions into one’s sense of self. This raises the question of which approach applies to VAs as teammates. The article extends the IS literature by examining the impact of VAs on individuals and teams and updates the knowledge on social identity and the extended self by deploying VAs in a collaborative setting. Using a laboratory experiment with N = 50, two groups were compared in solving a task, where one group was assisted by a VA, while the other was supported by a person. Results highlight that employees who identify VAs as part of their extended self are more likely to identify with team members and vice versa. The two aspects are thus combined into the proposed construct of virtually extended identification explaining the relationships of collaboration with VAs. This study contributes to the understanding on the influence of the extended self and social identity on collaboration with VAs. Practitioners are able to assess how VAs improve collaboration and teamwork in mixed teams in organizations.</jats:p>

Review papers are essential for knowledge development in IS. While some are cited twice a day, others accumulate single digit citations over a decade. The magnitude of these differences prompts us to analyze what distinguishes those reviews that have proven to be integral to scientific progress from those that might be considered less impactful. Our results highlight differences between reviews aimed at describing, understanding, explaining, and theory testing. Beyond the control variables, they demonstrate the importance of methodological transparency and the development of research agendas. These insights inform all stakeholders involved in the development and publication of review papers.

Literature reviews (LRs) play an important role in the development of domain knowledge in all fields. Yet, we observe a lack of insights into the activities with which LRs actually develop knowledge. To address this important gap, we (1) derive knowledge building activities from the extant literature on LRs, (2) suggest a knowledge-based typology of LRs that complements existing typologies, and (3) apply the suggested typology in an empirical study that explores how LRs with different goals and methodologies have contributed to knowledge development. The analysis of 240 LRs published in 40 renowned IS journals between 2000 and 2014 allows us to draw a detailed picture of knowledge development achieved by one of the most important genres in the IS field. An overarching contribution of our work is to unify extant conceptualizations of LRs by clarifying and illustrating how LRs apply different methodologies in a range of knowledge building activities to achieve their goals with respect to theory.

While the analysis and usage of data are increasing in importance, the application of sophisticated BI solutions in small stores is limited by available technical capabilities and financial resources. This study investigates how brick-and-mortar stores can benefit from an expansion of service functionalities of a cross-industry loyalty card provider. Digitalizing the loyalty program created new opportunities, while the analysis of shopping data of 13 years, 19,000 customers, and 55 shops empowered data-based decision support.

Today, organizations must deal with a plethora of IT security threats and to ensure smooth and uninterrupted business operations, firms are challenged to predict the volume of IT security vulnerabilities and allocate resources for fixing them. This challenge requires decision makers to assess which system or software packages are prone to vulnerabilities, how many post-release vulnerabilities can be expected to occur during a certain period of time, and what impact exploits might have. Substantial research has been dedicated to techniques that analyze source code and detect security vulnerabilities. However, only limited research has focused on forecasting security vulnerabilities that are detected and reported after the release of software. To address this shortcoming, we apply established methodologies which are capable of forecasting events exhibiting specific time series characteristics of security vulnerabilities, i.e., rareness of occurrence, volatility, non-stationarity, and seasonality. Based on a dataset taken from the National Vulnerability Database (NVD), we use the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to measure the forecasting accuracy of single, double, and triple exponential smoothing methodologies, Croston's methodology, ARIMA, and a neural network-based approach. We analyze the impact of the applied forecasting methodology on the prediction accuracy with regard to its robustness along the dimensions of the examined system and software package "operating systems", "browsers" and "office solutions" and the applied metrics. To the best of our knowledge, this study is the first to analyze the effect of forecasting methodologies and to apply metrics that are suitable in this context. Our results show that the optimal forecasting methodology depends on the software or system package, as some methodologies perform poorly in the context of IT security vulnerabilities, that absolute metrics can cover the actual prediction error precisely, and that the prediction accuracy is robust within the two applied forecasting-error metrics.

<jats:p>The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.</jats:p>

This interview is part of the special issue (01/2020) on “High Performance Business Computing” to be published in the journal Business & Information Systems Engineering. The interviewee Utz-Uwe Haus is Senior Research Engineer @ CRAY European Research Lab (CERL)). A bio of him is included at the end of the interview.

<jats:p> In recent times societal crises such as the coronavirus disease 2019 outbreak have given rise to a tension between formal ‘command and control’ and informal social media activated self-organising information and communication systems that are utilised for crisis management decision-making. Social media distrust affects the dissemination of disaster information as it entails shifts in media perception and participation but also changes in the way individuals and organisations make sense of information in critical situations. So far, a little considered notion in this domain is the concept of sense-giving. Originating from organisational theory, it is used to explain the mechanisms behind intentional information provision that fosters collective meaning creation. In our study, we seek to understand the potential impact of sense-giving from Twitter crisis communication generated during the Hurricane Harvey disaster event. Social network and content analyses performed with a dataset of 9,414,463 tweets yielded insights into how sense-giving occurs during a large-scale disaster event. Theoretically, we specified (1) perpetual sense-giving, which relies primarily on topical authority and frequency; as well as (2) intermittent sense-giving, which occurs from high value of message content and leverage of popularity, that is, retweets. Our findings emphasise the importance of information-rich actors in communication networks and the leverage of their influence in crises such as coronavirus disease 2019 to reduce social media distrust and facilitate sense-making. </jats:p>

Timing plays a crucial role in the context of information security investments. We regard timing in two dimensions, namely the time of announcement in relation to the time of investment and the time of announcement in relation to the time of a fundamental security incident. The financial value of information security investments is assessed by examining the relationship between the investment announcements and their stock market reaction focusing on the two time dimensions. Using an event study methodology, we found that both dimensions influence the stock market return of the investing organization. Our results indicate that (1) after fundamental security incidents in a given industry, the stock price will react more positively to a firm’s announcement of actual information security investments than to announcements of the intention to invest; (2) the stock price will react more positively to a firm’s announcements of the intention to invest after the fundamental security incident compared to before; and (3) the stock price will react more positively to a firm’s announcements of actual information security investments after the fundamental security incident compared to before. Overall, the lowest abnormal return can be expected when the intention to invest is announced before a fundamental information security incident and the highest return when actual investing after a fundamental information security incident in the respective industry.

In disaster operations management, a challenging task for rescue organizations occurs when they have to assign and schedule their rescue units to emerging incidents under time pressure in order to reduce the overall resulting harm. Of particular importance in practical scenarios is the need to consider collaboration of rescue units. This task has hardly been addressed in the literature. We contribute to both modeling and solving this problem by (1) conceptualizing the situation as a type of scheduling problem, (2) modeling it as a binary linear minimization problem, (3) suggesting a branch-and-price algorithm, which can serve as both an exact and heuristic solution procedure, and (4) conducting computational experiments - including a sensitivity analysis of the effects of exogenous model parameters on execution times and objective value improvements over a heuristic suggested in the literature - for different practical disaster scenarios. The results of our computational experiments show that most problem instances of practically feasible size can be solved to optimality within ten minutes. Furthermore, even when our algorithm is terminated once the first feasible solution has been found, this solution is in almost all cases competitive to the optimal solution and substantially better than the solution obtained by the best known algorithm from the literature. This performance of our branch-and-price algorithm enables rescue organizations to apply our procedure in practice, even when the time for decision making is limited to a few minutes. By addressing a very general type of scheduling problem, our approach applies to various scheduling situations.

Business model innovation is typically taught in small seminars at universities. Teaching this intrinsically task-oriented subject to a large number of students is a challenge. In this paper we address this challenge by proposing an experiential and interactive approach to teaching business models in a large classroom setting.

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>Leveraging social action theory, social network theory and the notion of network externality, the purpose of this paper is to model two different return on investment (ROI) measures: the networked ROI which captures the network effect originating from a social media investment, and the discrete ROI which focuses social media discrete returns from individual users.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>A field experiment was set up over a period of three months to test the effects of two variants of an advertisement campaign (a social vs a discrete ad) on the modeled networked and discrete ROIs.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>The authors find that emphasizing discrete user actions leads to lower network gains, but higher monetary returns while the social action emphasis produces higher network gains, but lower monetary returns. The study further suggests that social action focus is preferable for brand promotion and engagement, whereas the discrete action focus is suitable for boosting sales and website traffic.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Practical implications</jats:title> <jats:p>Several potential implications for social media researchers and marketers are also discussed.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>The authors for the first time showed that that the social media returns are derived not only from individual actions taken by the user (e.g. likes and shares) but also from users’ social interdependencies and the additional exposure that results from network effects.</jats:p> </jats:sec>

Scheduling problems are essential for decision making in many academic disciplines, including operations management, computer science, and information systems. Since many scheduling problems are NP-hard in the strong sense, there is only limited research on exact algorithms and how their efficiency scales when implemented on parallel computing architectures. We address this gap by (1) adapting an exact branch-and-price algorithm to a parallel machine scheduling problem on unrelated machines with sequence- and machine-dependent setup times, (2) parallelizing the adapted algorithm by implementing a distributed-memory parallelization with a master/worker approach, and (3) conducting extensive computational experiments using up to 960 MPI processes on a modern high performance computing cluster. With our experiments, we show that the efficiency of our parallelization approach can lead to superlinear speedup but can vary substantially between instances. We further show that the wall time of serial execution can be substantially reduced through our parallelization, in some cases from 94 hours to less than six minutes when our algorithm is executed on 960 processes.

Consumer ratings play a decisive role in purchases by online shoppers. Although the effect of the average and the number of consumer ratings on future product pricing and demand have been studied with some conclusive results, the effects of the variance of these ratings are less well understood. We develop a model which considers durable goods that are characterized by three types of attributes: search attributes, experience attributes, and transformed attributes the latter are conventional experience attributes that are transformed by consumer ratings into attributes that can be searched. Using informed search attributes to refer to the combination of search attributes and transformed attributes, we consider two sources of variance of consumer ratings: taste differences about informed search attributes and quality differences in the form of product failure representing experience attributes. We find that (i) optimal price increases and demand decreases in variance caused by informed search attributes, (ii) optimal price and demand decrease in variance caused by experience attributes, and (iii) by holding the average rating as well as the total variance constant, for products with low total variance price and demand increase in the relative share of variance caused by informed search attributes. Counter to intuition, we demonstrate that risk averse consumers may prefer a higher priced product with a higher variance in ratings when deciding between two similar products with the same average rating. Finally, our model provides a theoretical explanation for the empirically observed j-shaped distribution of consumer ratings in e-commerce that differs from established explanations.

In reaction to the productivity challenges that hospitals around the world have faced, some hospitals have begun to move towards a process-oriented organization of care in order to enhance productivity. Existing research on process-oriented organization emphasizes severe challenges along the implementation process. However, the literature contains only a small number of documented cases of hospital-wide process-oriented reorganization. Against this background, in this case study, we explain how hospitals can successfully implement organization-wide process orientation. To do so, we conducted an exploratory single case study with semi-structured, face-to-face interviews and document analyses as our primary data-collection methods. We developed a theoretical framework of antecedents, interventions, enablers, barriers, and consequences that explain the trajectory of this successful hospital-reorganization project. We contribute a substantive theory on which other researchers can build and can extend in future studies. Further, in analyzing our unique case, we identify factors that the extant literature has not yet discussed, such as the blackboxing of diagnosis and treatment activities as an enabler. In line with existing literature, we also found that, even in this case, inflexible healthcare IT represented a barrier that hindered the case study in implementing process orientation.

The need to protect resources against attackers is reflected by huge information security investments of firms worldwide. In the presence of budget constraints and a diverse set of assets to protect, organizations have to decide in which IT security measures to invest, how to evaluate those investment decisions, and how to learn from past decisions to optimize future security investment actions. While the academic literature has provided valuable insights into these issues, there is a lack of empirical contributions. To address this lack, we conduct a theory-based exploratory multiple case study. Our case study reveals that (1) firms? investments in information security are largely driven by external environmental and industry-related factors, (2) firms do not implement standardized decision processes, (3) the security process is perceived to impact the business process in a disturbing way, (4) both the implementation of evaluation processes and the application of metrics are hardly existent and (5) learning activities mainly occur at an ad-hoc basis.

Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.

Taxi ridesharing1 (TRS) is an advanced form of urban transportation that matches separate ride requests with similar spatio-temporal characteristics to a jointly used taxi. As collaborative consumption, TRS saves customers money, enables taxi companies to economize use of their resources, and lowers greenhouse gas emissions. We develop a one-to-one TRS approach that matches rides with similar start and end points. We evaluate our approach by analyzing an open dataset of > 5 million taxi trajectories in New York City. Our empirical analysis reveals that the proposed approach matches up to 48.34% of all taxi rides, saving 2,892,036 km of travel distance, 231,362.89 l of gas, and 532,134.64 kg of CO2 emissions per week. Compared to many-to-many TRS approaches, our approach is competitive, simpler to implement and operate, and poses less rigid assumptions on data availability and customer acceptance.

Digital interactions among businesses and consumers through powerful information systems and omnipresent connected devices establish today’s networked society. In this light, Service Science continues to take root as a research discipline that focuses on the integration of (digital) resources by service providers and service customers for value co-creation in service systems. Rapid advances in information technology allow for designing novel information systems that enable entirely new configurations of service systems. In turn, Service Science also leaves its mark on the design, adoption, and use of information systems and technology. With this special issue, we compile a set of timely papers that investigate selected facets of the complex interplay between information technology, information systems, and Service Science to design innovative IT artifacts for smart service. This editorial opens this special issue by elaborating on our understanding of smart service.<br