Die Bau- und Handwerksbranche zeigt seit Jahren eine verhältnismäßig niedrige Produktivitätssteigerung, verbunden mit einem geringen Digitalisierungsgrad. Angesichts von Mitarbeiterknappheit, zunehmender Komplexität von Bauprojekten und steigenden Individualisierungsanforderungen der Endkunden scheint die Digitalisierung einer der Schlüsselfaktoren zur Verbesserung der Produktivität zu sein. Der vorliegende Beitrag analysiert aus zwei strategischen Perspektiven die sich abzeichnenden Entwicklungen in dieser Branche: eine, die bestehende Wertschöpfungslogik und akteurszentrierte Sichtweise, und eine zukunftsweisende Perspektive auf plattformbasierte, service- und informationszentrierte Dienstleistungs-Ökosysteme. Die strategischen Perspektiven werden aus institutioneller Sichtweise eines Großhandelsbetriebs untersucht, welcher als zentraler Intermediär aufgrund zahlreicher Herausforderungen durch Disintermediation gefährdet ist. Die Re-Intermediation digitaler Plattformen und der Wandel hin zu serviceorientierten Geschäftsmodellen könnten disruptiv für die Leistungserstellung sein. Wenngleich eine solche Transition neue Wertschöpfungsansätze verspricht, ergeben sich für innovative Koordinations‑, Kommunikations- und Integrationskonzepte neue Herausforderungen. Basierend auf der strategischen Analyse werden die zentralen Herausforderungen, welche die mangelnde Etablierung der Technologisierung in der Bau- und Handwerksbranche zum Teil bedingen, exemplarisch anhand der Abbildung von Variantenartikeln im Stammdatenmanagement und der Auftragserfassung analysiert. Die zunehmende Variantenvielfalt in der Bau- und Handwerksbranche kann stammdatenseitig über konfigurierbare Materialien abgebildet werden. Diese Materialart bietet für Vertriebsmitarbeiter und Kunden eine Variantenkonfiguration anhand vorab definierter Merkmale. In der Belegerfassung sind zukünftig vielfältige Geschäftsvorfälle und Kundentypen zu integrieren, um das Tagesgeschäft im Vertrieb bestmöglich zu unterstützen. Die skizzierten Herausforderungen müssen durch Standardisierung und intelligente IT-Systeme bzw. Stammdatenkonzepte gelöst werden, um die konstatierten Produktivitätsdefizite überwinden zu können und die Digitalisierung der Bauhandwerker zu stärken.

The construction and crafts sector has seen a relatively low increase in productivity for years, combined with a low level of digitalization. In light of labor shortages, the increasing complexity of construction projects, and rising demands for customization from end customers, digitalization appears to be a key factor in enhancing productivity. We analyze the emerging developments in this sector taking two strategic perspectives: one that preserves existing value creation logic and actor-centric views, and another that looks forward to platform-based, service- and information-centric business ecosystems. The strategic perspectives are explored from the institutional viewpoint of a wholesale business, which, as a central intermediary, faces potential threats from disintermediation. The re-intermediation through digital platforms and the shift towards service-oriented business models could be disruptive, transforming traditional value chains into network-like structures. While this transition promises new value creation approaches, it also poses fresh challenges for innovative coordination, communication, and integration concepts. Based on this strategic analysis, key challenges impeding the full adoption of technology in the construction and trades sector are examined, particularly through the lens of variant article representation in master data management and order processing. The increasing variety of construction materials can be managed through configurable materials in master data, allowing for predefined variant configurations by sales staff and customers. Future document processing must integrate diverse business transactions and customer types to support sales operations effectively. Addressing these challenges through standardization and advanced IT systems is essential for overcoming productivity deficits and enhancing digitalization in the trade sector.

Digital communication has become an essential part of both personal and professional contexts. However, unique characteristics of digital communication—such as lacking non-verbal cues or time asynchrony—clearly distinguish this new form of communication from traditional face-to-face communication. These differences raise questions about the transferability of previous findings on traditional communication to the digital communication context and emphasize the need for specialized research. To support and guide research on the analysis of digital communication, we conducted a systematic literature review encompassing 84 publications on digital communication in leading journals. By doing so, we provide an overview of the current body of research. Thereby, we focus on distinct fields of communication, methods used to collect and analyze digital communication data, as well as common methodological limitations. Building on these insights, we derive a series of comprehensive guidelines from five distinct areas for the collection and analysis of digital communication that can guide future research and organizational practice.

Massive open online courses (MOOCs) have significantly transformed e-learning by providing global accessibility, collaborative learning opportunities, and high-quality educational content. However, MOOCs face limitations such as limited social interaction, delayed feedback, and linguistic barriers. To overcome these challenges, there is a growing interest in research to leverage artificial intelligence (AI) technologies. In this study, we conduct a systematic literature review examining the capabilities of generative AI (GenAI) and its implications for e-learning in MOOCs. We identify ten capabilities of GenAI, including analytical processing, generative, (personalized) assistant, support, feedback, assessment, communication, reflection, adaptivity, and accessibility. The findings highlight GenAI’s potential to overcome challenges in MOOCs by providing insightful and contextually relevant responses, generating new content, facilitating multilingual interactions, and enhancing the overall e-learning experience. We therefore contribute to the broader understanding of GenAI’s impact on e-learning and suggest avenues for future research on GenAI and its integration in MOOCs.

The price is the most important determinant for product sales and is highly influential for a company's success. Nevertheless, price determination often follows individuals’ rules of thumb augmented with product and economic performance indicators. With the increasing dissemination of artificial intelligence in organizations and society, the accuracy of price determination in retail might be

enhanced by sophisticated pricing algorithms. Technological developments further increase the number of pricing algorithms and pricing tools available. Against this backdrop, we applied Nickerson et al.’s (2013) approach, proposing a taxonomy for describing pricing algorithms in retail. The taxonomy

consists of 19 dimensions and 59 characteristics. Analyzing 70 pricing tools revealed a high specialization for selected retail domains, a focus on competitor monitoring and dynamic pricing, and a minor use of current machine learning techniques. This is a first attempt at structuring pricing algorithms and developing a price management toolbox that constructs artificial intelligence-enabled pricing algorithms.

This study examines the effects of integrating a technically advanced and more human-like large language model into a voice assistant to assess, how technical advancements mitigate user annoyances. Therefore, a generative pre-trained transformer was integrated into Siri and made available to 23 interview participants. Preliminary results reveal a decrease in user-reported annoyances, showing that the integration not only improves technical accuracy but also enhances the perceived humanness of interactions. However, subsequent interviews indicated that the distinction between the effects of technical advancements and the infusion of humanness emerged as critical, indicating a complex interplay between these factors. It is therefore planned to differentiate between technical and human improvements in the further development of this article. The results contribute to the discourse on optimizing voice assistants by pinpointing the reduction of user annoyances as a pivotal factor in improving user experience, suggesting pathways for future enhancements in voice assistant platforms.

The increasing online competition, associated changes in customer behaviors, and effects of the pandemic in recent years have led to increasing retail store closures. This development has given rise to a downward spiral in terms of a decreasing attractiveness of local shopping places and a further reduction of stores. Research has recognized that smart services can unleash the potential to compensate for the competitive disadvantages of physical retailers by combining tailored physical and digital offerings to enhance customer-oriented value creation. However, most approaches are limited to in-store services without addressing the wider shopping experience in retail surroundings. Therefore, this paper provides a classification framework for smart services in retail evaluated against 163 use cases, as well as six service archetypes. This work contributes to understanding relevant service design elements and proposes applying the idea of a holistic customer experience to service design in physical retail environments.

Background: Online pharmacies are used less than other e-commerce sites in Germany. Shopping behavior does not correspond to consumption behavior, as online purchases are predominantly made for over-the-counter (OTC) medications. Objective: The objective of this study was to understand the purchasing experiences of online pharmacy customers in terms of critical factors for online pharmacy adoption. Methods: This study examined the perceived risk, perceived trust, and emotions related to purchasing medications online and, consequently, the purchase intention toward online pharmacies. In a within-subjects design (N=37 participants), 2 German online pharmacies with different perceptions of risk and trust were investigated for their main business, namely OTC and prescription drugs. The results of a preliminary study led to 1 online pharmacy with high and 1 with significantly low self-reported risk by the prestudy sample. Emotions were measured with a multimethod approach during and after the purchase situation as follows: (1) neural evaluation processes using functional near-infrared spectroscopy, (2) the automated direct motor response during the use of the online pharmacy via facial expression analysis (FaceReader), and (3) subjective evaluations through self-reports. Following the shopping experiences at both pharmacies for both product types, risk, trust, and purchase intention toward the pharmacies were assessed using self-assessments. Results: The 2 online pharmacies were rated differently in terms of risk, trust, emotions, and purchase intention. The high-risk pharmacy was also perceived as having lower trust and vice versa. Significantly stronger negative emotional expressions on customers' faces and different neural activations in the ventromedial prefrontal cortex and dorsomedial prefrontal cortex were measured when purchasing prescription drugs from the high-risk pharmacy than from the low-risk pharmacy, combined with OTC medications. In line with this, customers' self-ratings indicated higher negative emotions for the high-risk pharmacy and lower negative emotions for the low-risk pharmacy. Moreover, the ratings showed lower purchase intention for the high-risk pharmacy. Conclusions: Using multimethod measurements, we showed that the preceding neural activation and subsequent verbal evaluation of online pharmacies are reflected in the customers' immediate emotional facial expressions. High-risk online pharmacies and prescription drugs lead to stronger negative emotional facial expressions and trigger neural evaluation processes that imply perceived loss. Low-risk online pharmacies and OTC medications lead to weaker negative emotional facial expressions and trigger neural evaluation processes that signify certainty and perceived reward. The results may provide an explanation for why OTC medications are purchased online more frequently than prescription medications.

The appropriate selection of suitable process models plays an important role for IT project success. To aid in decision-making, IT project management literature offers a plethora of decision models for selecting suitable process models, however, hybrid process models are often neglected and adoption in practice is low or non-existent. To address this challenge, we draw on contingency theory to develop and implement a tool-supported decision model for the selection and evaluation of appropriate process models for IT projects, thereby leveraging artificial intelligence and machine learning in the context of a self-enforcing network. Our model provides an objective tool to assess process model suitability. Results from a conducted online survey with project management experts indicate high validity. Therefore, we contribute to the field of IT project management by expanding AI-based decision models for selecting and evaluating process models through extending the range of covered models and implementing inherent weighting of criteria.

Machine learning (ML) has the potential to take on a variety of routine and non-routine tasks in brick-and-mortar retail and e-commerce. Many tasks previously executed manually are amenable to computerization using ML. Although procedure models for the introduction of ML across industries exist, the tasks for which ML can be implemented in retail need to be determined. To identify these application areas, we followed a dual approach. First, we conducted a structured literature review of 225 research papers to identify possible ML application areas in retail, as well as develop the structure of a well-established information systems architecture. Second, we triangulated these preliminary application areas with the analysis of eight expert interviews. In total, we identified 21 application areas for ML in online and offline retail; these application areas mainly address decision-oriented and economic-operative tasks. We organized the application areas in a framework for practitioners and researchers to determine appropriate ML use in retail. As our interviewees provided information at the process level, we also explored the application of ML in two exemplary retail processes. Our analysis further reveals that, while ML applications in offline retail focus on the retail articles, in e-commerce the customer is central to the application areas of ML.

The identification and satisfaction of user needs in business-to-consumer ecommerce is crucial to elicit positive experiences and establish loyalty toward the business. One major interface to the consumer is the website of the business, which can impact the user and customer experience throughout the whole journey. Current approaches that aim to measure user experience (UX) and related constructs have significant shortcomings and are often not able to uncover spontaneous processes in website users. Related neuroIS research proposes that a great part of behavior is formed by these spontaneous processes and that the utilization of additional, neurophysiological measurements can help to uncover these processes and better understand user behavior. Therefore, and to address the shortcomings of common UX measurements in ecommerce, this cumulative thesis presents 6 essays along different UX phases being pre-use, during-use, and post-/repeated-use of the ecommerce website. As a means to assess the spontaneous, potentially unconscious processes, the neuroimaging method fNIRS is utilized in the included essays. Across all papers, several responses to website design factors across UX phases could be identified on the neural level. The complementing self-reported data could reveal these effects only to a certain degree. Consequently, this thesis shows the contribution of neuroimaging (fNIRS) to UX investigation on ecommerce websites to better understand users, which helps to optimize website design and thus, UX.

Manipulating their environment is one of the fundamental actions that humans, and actors more generally, perform. Yet, today’s mixed reality systems enable us to situate virtual content in the physical scene but fall short of expanding the visual illusion to believable environment manipulations. In this paper, we present the concept and system of Scene Responsiveness, the visual illusion that virtual actions affect the physical scene. Using co-aligned digital twins for coherence-preserving just-in-time virtualization of physical objects in the environment, Scene Responsiveness allows actors to seemingly manipulate physical objects as if they were virtual. Based on Scene Responsiveness, we propose two general types of end to-end illusionary experiences that ensure visuotactile consistency through the presented techniques of object elusiveness and object rephysicalization. We demonstrate how our Daydreaming illusion enables virtual characters to enter the scene through a physically closed door and vandalize the physical scene, or users to enchant and summon far-away physical objects. In a user evaluation of our Copperfield illusion, we found that Scene Responsiveness can be rendered so convincingly that it lends itself to magic tricks. We present our system architecture and conclude by discussing the implications of scene-responsive mixed reality for gaming and telepresence.

As the importance of machine learning (ML) in technology has increased over past years, it is important to understand each application and its implications for ML individually. As a sample application of ML, demand forecasts occupy a special position in industry and retail and are used in numerous optimization activities, such as price optimization. In past years, demand forecasting has transformed the way retailers and industry replenish their goods. From a manual process, forecast-driven replenishment processes are now used. While this process has very few requirements for the ML-based demand forecast, this changes when further processes are transformed. While optimizing prices, demand forecasting requires elasticity regarding optimization objectives. When ML methods are used for demand forecasting, there must be sufficient data to learn the proper elasticity requirements. To overcome the problem of missing historical data resulting in low elasticity and thus bad optimization performance, we present a method for transferring knowledge between articles and companies to train shared ML models. For the first time, we are able to optimize prices without generating historical demand data for all possible prices. Evaluations of a design science project show that federated learning with neural networks achieves good forecasting accuracy and high elasticity.

Die Entscheidungen im Marketingmix und besonders jene über Preis und Platzierung waren im Handel stets von enormer Bedeutung, aber bisher wenig automatisiert oder datengetrieben. Dies liegt darin begründet, dass es bisher keine Notwendigkeit dafür gab und ein Mangel an informationstechnischen Möglichkeiten limitierend wirkte. Aufgrund der zunehmenden Online-Affinität der Kunden und der dort nun schon seit einigen Jahren zu beobachtenden Preisvariationen hat sich dort ein anderes Preisgeschehen etabliert. Die vorgestellte Fallstudie zeigt auf, wie bisher nicht verfügbare Daten erfasst und genutzt werden können, um mit der Verknüpfung der Variablen Preis und Platzierung neuartige Entscheidungen zu treffen. Dennoch bleiben die Komplexität und Menge an Entscheidungen im Single-Channel-Handel auf wenige Entscheidungen begrenzt. Sobald nun aber mehrere Kanäle im Omni-Channel …

While standardized enterprise systems (ES) have become widely accepted, this is not the case for machine learning (ML) implementations, which are mostly developed individually in company-specific projects. Necessary historical data and rare ML capabilities result in a low cross-market ML utilization. To overcome the high usage barriers of ML, it should be incorporated into ES in a standardized manner. Therefore, we propose to implement an ML marketplace. While marketplaces in ES already exist, this paper proposes a marketplace dedicated to the exchange of ML models in a federated learning approach. Accordingly, this work formulates four meta requirements based on interviews, which are structured by marketplace governance dimensions. With these meta-requirements, an ML marketplace was implemented in a design science research project, from which eight design principles are derived. The design principles address governance dimensions for making ML accessible to many companies and allow them to integrate ML into existing ES.

Platform ecosystems have captured a variety of markets, enabling coordination, transactions, and value co-creation between independent actors. A focal platform constitutes the central nexus of e-commerce ecosystems and fosters the interaction among ecosystem participants through their boundary resources. Standardizing these interfaces simplifies ecosystem entry for developers and increases the number of participants propelling the network effects, and thus the overall value of the ecosystem. Currently, there is a lack of prescriptive design knowledge guiding platform owners in designing successful e-commerce ecosystems. Addressing this issue, we followed a dual approach, reporting on a systematic literature review in which we identified design requirements and complemented these with a multiple-case study on selected e-commerce ecosystems. Aggregating the requirements resulted in six meta-requirements and 19 design principles that foster the standardization of focal e-commerce platforms. Our design principles simplify the development of complements and enable multi-homing for developers due to possible standardization across ecosystems.

Der Einsatz von IT-Systemen ist omnipräsent. Im Gegensatz zur Verbreitung der Systeme werden die Wirkungen der Systeme für Individuen, Unternehmen oder Gesellschaften selten betrachtet. Damit dürften viele Nutzer und Unternehmen von der Wirkung der Investition überrascht werden. Die Gefahr fehlerhafter Investitionen nimmt zu und mögliche Potenziale der IT bleiben ungenutzt.

Das Buch vermittelt wissenschaftlich fundiertes Wissen über die Wirkung und Wirtschaftlichkeit von IT-Systemen. Aufbauend auf Grundlagen aus der Organisationslehre, Soziologie, Entscheidungstheorie, Kosten- und Leistungsrechnung wird diskutiert, welche Phänomene durch IT-Systeme induziert werden. Die mannigfaltigen Wirkungen von IT-Systemen sowie Verfahren zur Wirkungsermittlung werden beschrieben. Sämtliche Aspekte für ein holistisches Wirkungsmanagement von IT-Systemen von der Projektskizze bis zur Umsetzung in der Realität werden in einem neuen Ansatz integriert.

An important economic sector influenced by the development of platforms is e-commerce. The most successful companies in e-commerce employ platform business models and strive to provide other companies with application services. Despite growing economic importance and rising research interest, thus far, no attempts were made to structure existing research into platforms in e-commerce. Hence, a quantitative bibliometric analysis of 7,463 platform-related papers in the context of e-commerce was conducted. The papers were published in major conferences, journals, and books from 1993 to 2021. The authors identified a continuous development of platform research in e-commerce, with the continuous development characterized by three major periods of research. Furthermore, four clusters in platform research are outlined, i.e., business models, social commerce, infrastructure, and socio-technical characteristics. These clusters can serve as a foundation for future research. The conducted bibliometric analysis contributes to scientific research by offering an objective and systematic overview of platform research in e-commerce.

The business value of IT in companies is a highly discussed topic in information systems research. While the IT business value is an agreed upon term, its decomposition and assessment on a more detailed level is ambiguous in literature and practice. However, assessing the IT business value is pivotal for goal-oriented IT management. Therefore, we suggest a hierarchical decomposition of the IT business value along aggregated impacts and atomic impacts. We introduce a taxonomy to gain a better understanding of what types of atomic impacts may be caused by IT investments. With the help of the taxonomy, we classify a total of 957 values from existing value catalogs and derive 29 archetypal IT impacts grouped by a company’s business units. Bundling this grouping with exemplary impacts for the IT value assessment, we finally propose an IT value meta-framework for the structured business value assessment.

Multi-sided markets (MSMs) have entered the retail sector as digital marketplaces and have proven to be a successful business model compared to traditional retailing. Established retailers are increasingly establishing MSMs and also participate in MSMs of pure online companies. Retailers transforming to digital marketplaces orchestrate formerly independent markets and enable retail transactions between participants while simultaneously selling articles from an own assortment to customers on the MSM. The retailer’s dual role must be supported by the retail information systems. However, this support is not explicitly  represented in existing reference architectures (RAs) for retail information systems. Thus, we propose to develop a RA for retail information systems facilitating the orchestration of supply- and demand-side participants, selling own articles, and providing innovation platform services. We apply a design science research approach and present seven architectural requirements that a RA for MSM business models needs to fulfill (dual role, additional participants, affiliation, matchmaking, variety of services, innovation services, and aggregated assortment) from the rigor cycle. From a first design iteration we propose three exemplary, conceptual architectural patterns as a solution for three of these requirements (matchmaking for participants, innovation platform services, and aggregated assortment).

Multi-sided markets (MSMs) have entered the retail sector as digital marketplaces and have proven to be a successful business model compared to traditional retailing. Established retailers are increasingly establishing MSMs and also participate in MSMs of pure online companies. Retailers transforming to digital marketplaces orchestrate formerly independent markets and enable retail transactions between participants while simultaneously selling articles from an own assortment to customers on the MSM. The retailer's dual role must be supported by the retail information systems. However, this support is not explicitly represented in existing reference architectures (RAs) for retail information systems. Thus, we propose to develop a RA for retail information systems facilitating the orchestration of supply-and demand-side participants, selling own articles, and providing innovation platform services. We apply a design science research approach and present seven architectural requirements that a RA for MSM business models needs to fulfill (dual role, additional participants , affiliation, matchmaking, variety of services, innovation services, and aggregated assortment) from the rigor cycle. From a first design iteration we propose three exemplary, conceptual architectural patterns as a solution for three of these requirements (matchmaking for participants, innovation platform services, and aggregated assortment).

Music is a central instrument in video gaming to attune a player’s attention to the current atmosphere and increase their immersion in the game. We transfer the idea of scene-adaptive music to car drives and propose SoundsRide, an in-car audio augmented reality system that mixes music in real-time synchronized with sound affordances along the ride. After exploring the design space of affordance-synchronized music, we design SoundsRide to temporally and spatially align high-contrast events on the route, e.g., highway entrances or tunnel exits, with high-contrast events in music, e.g., song transitions or beat drops, for any recorded and annotated GPS trajectory by a three-step procedure. In real-time, SoundsRide 1) estimates temporal distances to events on the route, 2) fuses these novel estimates with previous estimates in a cost-aware music-mixing plan, and 3) if necessary, re-computes an updated mix to be propagated to the audio output. To minimize user-noticeable updates to the mix, SoundsRide fuses new distance information with a filtering procedure that chooses the best updating strategy given the last music-mixing plan, the novel distance estimations, and the system parameterization. We technically evaluate SoundsRide and conduct a user evaluation with 8 participants to gain insights into how users perceive SoundsRide in terms of mixing, affordances, and synchronicity. We find that SoundsRide can create captivating music experiences and positively as well as negatively influence subjectively perceived driving safety, depending on the mix and user.

Colors surround us in all life situations and have the power to influence our mood and emotions, which makes color a crucial impact factor for decision making in ecommerce environments. Therefore, this research paper aims to explore the psychological and physiological effects of color use on websites with a neuroimaging method. For this, an experiment was designed in which 24 healthy participants watched an online shoe shop in 4 different colored versions during which their neural activity in the prefrontal cortex was measured. While comparing the neural activity between the different employed colors, distinct implications can be derived. That is, in comparison to the blueish website, (1) grey scaled websites require higher cognitive load but are rated as aesthetically pleasing, (2) greenish websites result in more negative valence while they require not more neural resources, and (3) reddish websites are rated low in valence and require higher neural processing resources.

Social robots increasingly diffuse into our lives in work, health, and many other areas. However, theoretical approaches that explain how social robots should be designed to maximize experiential and performance-related outcomes in human-robot interaction (HRI) are still rare. To close this research gap, we aim to develop a dual process model of HRI with the help of two experiments. Results of the first experiment show that individuals categorize humans and robots differently in the automatic and reflective system, leading to different forms of robotic biases in these systems. Specifically, whereas humans show a bias against all types of robots in the reflective system, they only show biases against robots with low anthropomorphism in the automatic system. With the second experiment, we aim to complement these results from a neurophysiological perspective to gain more insights into cognitive processes during classification and evaluation of robots.

Blue of all colors seems to be generally preferred by humans and animals. Consequently, the use of this color in ecommerce context has several positive effects such as increased trustworthiness and aesthetic ratings. These effects are, in this study, hypothesized to be caused by specific neural processes in the prefrontal cortex of human decision makers. Consequently, this study tackles the research question whether there is a distinct neural activation pattern for blue websites that helps to explain why blue is often most favored. To investigate this, one website is designed and manipulated in col-or to which user reactions are measured by employing functional near-infrared spectroscopy (fNIRS). The results of this study show that blue colored websites seem to require generally less processing power related to cognitive while revealing increases in brain structures related to processing pleasant and aesthetic stimuli.
 

Whether males and females evaluate ecommerce websites differently has long been discussed and has resulted in inconsistent research findings. While some studies identified gender differences in the evaluation of websites, other studies indicate that these differences are inexistent. To shed light on these hypothetical gender differences on ecommerce website perceptions, a behavioral and functional near-infrared spectroscopy (fNIRS) experiment in which participants had to use and evaluate three different ecommerce websites was conducted. While the questionnaire-based behavioral results showed no significant differences between gender, neural gender differences could be discovered. In particular, well rated websites resulted in increased neural activity for men in brain regions of the dlPFC and vlPFC in the left hemisphere, while the lower evaluated websites resulted in an increased neural activity in brain regions of the vmPFC for men in the right hemisphere. Consequently, the results suggest that men seem to require higher neural activity for the emotional appraisal of, and decision making on ecommerce websites.

The ever increasing intra- and inter-competition, increased costs and a lower differentiation margin have led to intense competitive pressures within the domains of wholesaling and retailing. At the same time the increasing digitization due to technological innovation will radical change the economical and procedural fundamentals. In addition, from an entrepreneurial point of view, it must also be considered that the complexity of decision-relevant framework conditions has increased. Digitalisation is therefore to be seen as an essential driver for optimising business processes and as the basis for a new understanding of products and services. However, it cannot be considered independently of other social trends. The chapter will elaborate the underlying drivers and technological trends empowering the transformation processes within the domain of wholesaling and reailing. Starting from these major drivers we will scrutinize the impact of technological advancements and the opportunities, risks and constraints which are connected with it.

Mit der digitalisierungsbedingten Zunahme kontinuierlich erfasster Massendaten aus unterschiedlichsten Quellen entsteht die Hoffnung, einen besseren Zugang als je zuvor zur Realität zu erhalten. Angesichts des Big-Data-Phänomens stellt sich dabei für Handelsunternehmen die Frage, welche unternehmensweite Datenstrategie es zu verfolgen gilt. Dazu liefert der folgende Beitrag anhand eines Frameworks eine sachlogische Struktur für eine unternehmensweite Datenstrategie. Für jedes aufgezeigte Strategiefeld dieses Frameworks sind dabei Basisentscheidungen zu treffen, die die nötigen Voraussetzungen zur Nutzung von Daten für erfolgreiche Einzelprojekte als auch die erfolgreiche Eingliederung datengetriebener Aktivitäten in Standardprozesse schaffen. Neben der Datenstrategie wird auch der realisierbare betriebswirtschaftliche Beitrag für Einzelhändler anhand einer Fallstudie entfaltet. Dazu wird auf das Handelsmarketing und den Marketing-Mix als zentrales Konzept, und seit jeher eines der wichtigsten Tätigkeitsfelder im Einzelhandel, zur Aufstellung einer datengetriebenen Entscheidungsphänomenologie zurückgegriffen. Als Antwort auf die Frage, welche Datenstrategie angesichts von Big Data von einem Einzelhändler zu verfolgen ist, wurden verschiedene Teilbereiche identifiziert und Maßnahmen innerhalb dieser ausgearbeitet. Es wurde gezeigt, dass integrierte und kohärente Maßnahmen auf organisationaler und technologischer Ebene für die Sammlung, Speicherung, Verarbeitung, Verwendung, Steuerung und Transformation von Big Data im Unternehmen nötig sind. Mit Erfüllung der notwendigen Vorrausetzungen für Big Data im Rahmen der Datenstrategie ist es dabei möglich nicht nur die hier aufgezeigte Absatzseite der Unternehmung zu optimieren, sondern auch die tiefgreifende Integration der verschiedenen Prozessbereiche im Handelsunternehmen abzubilden.

Information technologies in general and artifical intelligence (AI) in particular try to shift operational task away from a human actor. Machine learning (ML) is a discipline within AI that deals with learning improvement based on data. Subsequently, retailing and wholesaling, which are known for their high proportion of human work and at the same time low profit margins, can be regarded as a natural fit for the application of AI and ML tools. This article examines the current prevalence of the use of machine learning in the industry. The paper uses two disparate approaches to identify the scientific and practical state-of-the-art within the domain: a literature review on the major scientific databases and an empirical study of the 10 largest international retail companies and their adoption of ML technologies in the domain are combined with each other. This text does not present a prototype using machine learning techniques. Instead of a consideration and comparison of the particular algorythms and approaches, the underling problems and operational tasks that are elementary for the specific domain are identified. Based on a comprehensive literature review the main problem types that ML can serve, and the associated ML techniques, are evaluated. An empirical study of the 10 largest retail companies and their ML adoption shows that the practical market adoption is highly variable. The pioneers have extensively integrated applications into everyday business, while others only show a small set of early prototypes. However, some others show neither active use nor efforts to apply such a technology. Following this, a structured approach is taken to analyze the value-adding core processes of retail companies. The current scientific and practical application scenarios and possibilities are illustrated in detail. In summary, there are numerous possible applications in all areas. In particular, in areas where future forecasts and predictions are needed (like marketing or replenishment), the use of ML today is both scientifically and practically highly developed.

Multi-sided markets (MSMs) have proven to be a successful business model in the dynamic electronic commerce environment. There exists a variety of MSMs differing in their provided features and services for their participants. Existing taxonomies often focus on value creation and business-to-business transactions. We apply Nickerson et al.’s taxonomy development approach. We especially incorporated aspects of orchestrating the distinct market sides and governance dimensions. The developed taxonomy for MSMs consists of 21 dimensions and 99 characteristics in total. We have applied our taxonomy to 44 MSMs and identified asymmetries between the market sides concerning Monetization, Network Effect Amplifiers and Provided Services. We emphasize that the taxonomy is not only an artifact for classifying the current situation of an MSM but can also be used by MSM owners to derive directions for the future development. We illustrated how these developments can be conducted by examples for five dimensions of our taxonomy