Miracle-dz/UniFAR-FTUs huggingface dataset:
A Value-based Perspective on User Experience - How Alexa's Value Dispositions Elicit Emotional Responses

Previous studies of user experiences with Conversational Agents (CAs) have often focused on specific use cases and evaluated usability and user expectations. Investigations of users’ emotional responses and on the value implications created by CAs have remained rare. We present a qualitative study with 30 participants, using a novel approach of observation that explores emotional responses to the underlying values elicited or harmed by the CA. Particularly, we report on the emotional responses to 37 unique values, which are involved when users perceive and interact with CAs or consider the social implications of CA usage. We found that harms of artefact-based values often continuously provoke anger and frustration. In contrast, our findings indicate that technology is particularly joyful when it fosters human values outside of central functionalities, such as community, autonomy, friendship, playfulness and humour. Manifestations of such person-based values can sustainably foster users’ personal growth, self-confidence and social relationships.
INSPIRATION BEFORE FOCUS – TIME-DEPENDENT INTEREST INSPIRATION BEFORE FOCUS – TIME-DEPENDENT INTEREST ALLOCATION AND IDEA INNOVATIVENESS IN ONLINE ALLOCATION AND IDEA INNOVATIVENESS IN ONLINE COMMUNITIES COMMUNITIES

Online communities such as crowdsourcing platforms or user innovation communities are valuable sources for innovation. Community members interact with each other to exchange their ideas. These social interactions express community members’ interests which may change over time. Building on prior research, we investigate a leading open hardware online community to analyse how community members’ time-dependent interest allocation influences their idea generation. Utilizing the topic modelling technique LDA to extract hidden knowledge elements from the idea descriptions, our findings suggest that it is favourable for community members to focus on specific domains after receiving inspiration to generate innovative ideas. This effect is further amplified for an increasing difference between broad and focused interest. With these findings, we contribute to the literature on IS, innovation, and social networks.
Evaluating the Value of Emerging Digital Platform Ecosystems: Lessons from the Construction Industry

Despite previous research on well-established digital platforms aimed at business-to-consumer markets, there is a limited understanding of how smaller actors of project-based industries create new business-to-business digital platform ecosystems. This paper explores how incumbent actors evaluate business value as they transition towards an emerging ecosystem. Based on two cases of construction companies in Finland and Norway, we describe how the actors’ perception of value changes during the early stages of their digital transformation process. We present a non-exhaustive list of criteria different than costs and revenue, which the actors relied upon to evaluate or measure the value of their emerging platform. From a theoretical perspective, we expand previous studies on digital platform ecosystems and value creation. From a practical perspective, we discuss how to adapt existing business models and manage new partnerships for an emerging ecosystem.
Making Physical Activity A Habit: Using Gamified Physical Activity Trackers to Sustain Behaviour

Gamified activity trackers are being used by millions of people to assist in changing their daily habits, increasing levels of physical activity and improving overall health and wellbeing. For such goals to be achieved, individuals need to sustain these new habits over time. While existing research has demonstrated that these devices can be effective in changing behaviour in the short term, little is known about their ability to aid in transitioning behavioural change into ongoing habit. This research-inprogress study is exploratory and studies how the use of gamified activity trackers may help in creating sustainable behavioural changes associated with physical activity. Employing the repertory grid technique, we interviewed 15 activity tracker users to explore the barriers to sustained exercise habits as well as the capability of activity trackers to produce long-term sustainable behavioural changes. The preliminary findings revealed gamified activity trackers were effective in temporarily increasing exercise behaviour, however they failed to address key habit-forming requirements (that of association development and context stability).
LEOC: A Principled Method in Integrating Reinforcement Learning and Classical Control Theory

There have been attempts in reinforcement learning to exploit a priori knowledge about the structure of the system. This paper proposes a hybrid reinforcement learning controller which dynamically interpolates a model-based linear controller and an arbitrary differentiable policy. The linear controller is designed based on local linearised model knowledge, and stabilises the system in a neighbourhood about an operating point. The coefﬁcients of interpolation between the two controllers are determined by a scaled distance function measuring the distance between the current state and the operating point. The overall hybrid controller is proven to maintain the stability guarantee around the neighborhood of the operating point and still possess the universal function approximation property of the arbitrary non-linear policy. Learning has been done on both model-based (PILCO) and model-free (DDPG) frameworks. Simulation experiments performed in OpenAI gym demonstrate stability and robustness of the proposed hybrid controller. This paper thus introduces a principled method allowing for the direct importing of control methodology into reinforcement learning.
An Efficient Tensor Completion Method Combining Matrix Factorization and Smoothness

In this paper, the low-complexity tensor completion (LTC) scheme is proposed to improve the efficiency of tensor completion. On one hand, the matrix factorization model is established for complexity reduction, which adopts the matrix factorization into the model of low-rank tensor completion. On the other hand, we introduce the smoothness by total variation regularization and framelet regularization to guarantee the completion performance. Accordingly, given the proposed smooth matrix factorization (SMF) model, an alternating direction method of multiple(ADMM-) based solution is further proposed to realize the efficient and effective tensor completion. Additionally, we employ a novel tensor initialization approach to accelerate convergence speed. Finally, simulation results are presented to confirm the system gain of the proposed LTC scheme in both efficiency and effectiveness.
Visual Analytics in Organizational Knowledge Creation: a Case Study

The conversion between tacit and explicit knowledge remains an often-discussed and highly relevant topic in organizational knowledge creation. Although prior research addresses this process, it primarily focuses on the conversion between tacit and explicit knowledge through social processes. This work discusses theories of organizational knowledge creation in the light of sociotechnical systems, and specifically extends them to the interaction between individuals and visual analytics systems that afford analytical decision making based on interactive visualization and knowledge discovery mechanisms. Based on related work, we develop a theoretical framework to explain novel mechanism for knowledge creation afforded by visual analytics systems. We evaluate our framework with a case study with one of the leading organizations in the automotive industry. Over the course of the case study, we observe and analyze interactions between domain experts and a newly introduced visual analytics sys-tem. Through our case study findings, we reveal novel mechanisms of organizational knowledge creation and discuss their implications.
From Exploitative Structures towards Data subject-Inclusive Personal Data Markets - a Systematic literature Review

Companies that offer digital services to users for free are tempted to market their digital by-catch, personal data, on rapidly evolving personal data markets (PDMs). This paper presents a systematic literature review on PDM instruments that actively involve data subjects. First, by reviewing and classifying existing data subject-inclusive PDM instruments, six categories of PDM instruments are identified. Second, the contribution of the identified instruments towards creating data subject-inclusive PDMs is analyzed using a six-dimensional normative ethics-based framework. The analysis outlines technical, legal, and political instruments that could shape exploitative PDMs to become more ethical and data subject-inclusive. This shall nudge scholars to develop instruments that change current market structures, thus fostering data subject-inclusivity and minimizing inequalities in PDMs. Furthermore, the paper shall guide policy makers and practitioners in setting morally adequate framework conditions to create more data subject-inclusive PDMs.
What makes a data-driven business model? A consolidated taxonomy

The usage of data to improve or create business models has become vital for companies in the 21st century. However, to extract value from data it is important to understand the business model. Taxonomies for data-driven business models (DDBM) aim to provide guidance for the development and ideation of new business models relying on data. In IS research, however, different taxonomies have emerged in recent years, partly redundant, partly contradictory. Thus, there is a need to synthesize the common ground of these taxonomies within IS research. Based on 26 IS-related taxonomies and 30 cases, we derive and define 14 generic building blocks of DDBM to develop a consolidated taxonomy that represents the current state-of-the-art. Thus, we integrate existing research on DDBM and provide avenues for further exploration of data-induced potentials for business models as well as for the development and analysis of general or industry-specific DDBM.
Evolution of B2B Platform Ecosystems: What Can Be Learned from Salesforce?

Platform ecosystems are complex ecologies of firms with individual competencies and collective objectives. The sustainable evolution of platform ecosystems is thereby contingent on taking advantage of the individual competencies of the ecosystem’s actors toward obtaining collective objectives. To learn more about platform ecosystem evolution and dynamics, we study Salesforce, a leading and thriving B2B platform ecosystem. We find that the ecosystem’s evolution was closely defined not only by the platform owner’s orchestrating initiatives, but also by its complementors’ and customers’ competencies and particularities. Specifically, we derive three distinct dimensions of evolution, namely the extension of the platform core technology, the extension of the platform’s functional scope, and the industry-specific specialization of the platform. We further identify three cross-dimension levers, namely proprietary developments, acquisitions, as well as partnerships and alliances, which were employed by the platform owner to drive its platform ecosystem’s evolution.
A Multi-Perspective Research for the Investigation of Software Requirements for a Digital Study Assistant

Digitalization in higher education is setting a trend. According to the latest NMC Horizon Report, virtual assistants are among the six most important future technologies in context of higher education. Digital study assistants (DSA) can shape the future of teaching and learning by open up new opportunities for students, organizational units and teachers. When introducing such technologies, it is essential to ensure that content and functions are designed in a user-oriented manner for sustainable use. It’s therefore necessary to investigate expectations, success factors and barriers, as they are crucial for the acceptance and long-term use of new technologies. The SIDDATA project is currently developing a DSA and is striving to take these factors into account during development. Therefore, requirement analyses were conducted with various stakeholders. With regard to the multidimensional perspective in software development, three consecutive studies were accomplished with students (end user) and organizational units from different universities.
Supervised fuzzy c-means techniques to solve the capacitated vehicle routing problem

Fuzzy C-Means (FCM) clustering technique is among the most effective partitional clustering algorithms available in the literature. The Capacitated Vehicle Routing Problem (CVRP) is an important industrial logistics and managerial NP-hard problem. Cluster-First Route-Second Method (CFRS) is one of the efficient techniques used to solve CVRP. In CFRS technique, customers are first divided into clusters in the first phase, then each cluster is solved independently as a Traveling Salesman Problem (TSP) in the second phase. This research is concerned with the clustering phase of CFRS, and TSP is then solved using a traditional optimization method. Three supervised FCM based techniques are proposed to solve the clustering phase at reduced cost via centroids (pre-FCM) initialization phase. The proposed pre-FCM initialization techniques are developed to be problem dependent. Hence, three initialization techniques are first developed using K-means technique, spatially equally distributed, and demand weighted center of mass. Then, a modified demand weighted fuzzy c-means objective function is employed to assign customers to clusters. To compare the performance of the proposed supervised FCM techniques, forty-two CVRP benchmark problems are solved using the traditional fuzzy C-means algorithm and the developed algorithms. Extensive comparisons are conducted between the traditional fuzzy C-means algorithm, the three proposed initialization techniques, and other fuzzy C-means techniques available in the literature. Results show that the proposed three initialization techniques are efficient in terms of solution quality and computational cost.
Physical Layer Secrecy Analysis of Multihop Hybrid Satellite-Terrestrial Relay Networks with Jamming

This paper explores the secrecy analysis of a multihop hybrid satellite-terrestrial relay network (HSTRN) with jamming, where one satellite source is aimed at communicating with destination users via multihop decode-and-forward (DF) terrestrial relays, in the existence of an eavesdropper. All the destination users are deployed randomly following a homogeneous Poisson point process (PPP) based on stochastic geometry. Each relay operates not only as a conventional DF relay to forward the received signal but also as a jammer to generate intentional interference to degrade the eavesdropper link, considering shadowed-Rician fading for legitimate link and wiretap link while Rayleigh fading for jamming link. To characterize the secrecy performance of the considered network, the accurate analytical expression for the secrecy outage probability (SOP) is derived. In order to reveal further insights on the achievable diversity order of the network, the asymptotic behavior of SOP expression at high signal-tonoise ratio (SNR) region is deduced. Moreover, the throughput of the system is discussed to characterize the secrecy performance. Finally, the theoretical results are validated through comparison with simulation results and show that (1) the secrecy performance of the considered network gets better with the decreasing of the hops and with the decreasing severity of the channel fading scenario, (2) the relay of the network operating as a jammer can provide better secrecy performance without extra network resources, and (3) small hops and high SNR can yield to high throughput of the system.
Structuring AI Resources to Build an AI Capability: a Conceptual Framework

Artificial Intelligence (AI) has gained traction over the past few years as the new frontier for gaining a competitive advantage. While firms have started investing heavily in AI, there is a growing disillusionment around the value that can be generated and the process through which that can be obtained. Building on this gap, we develop a conceptual framework that builds on resource orchestration theory. The framework distinguishes between the ideation of AI capabilities and the implementation of AI capabilities and present how activities related to resource orchestration theory are relevant in the context of AI deployments. We develop a set of propositions on the activities that underlie the main processes around resource orchestration of AI, and present a research design to actualize the research plan.
Understanding the adoption of cyber insurance for residual risks - An empirical large-scale survey on organizational factors of the demand side

This research paper analyzes technological, organizational, and environmental (TOE framework) adoption factors of cyber insurances (CI) by conducting a computer-assisted telephone interview study with 2,483 German firms. Considering our screening of related literature, this study, to our knowledge, is the first large-scale empirical study analyzing organizational adoption factors of CI on the demand side. We distinguish between firms that have or have not considered CI and those that have or have not adopted CI following considerations. Our regression results indicate that there are statistically significant factors on the consideration and adoption of CI across all TOE dimensions. Subsequently, we discuss the extent to which CI is perceived as an appropriate tool to manage information security and derive propositions for the education of firms and further research in academia.
LET’S DO DESIGN THINKING VIRTUALLY: DESIGN AND LET’S DO DESIGN THINKING VIRTUALLY: DESIGN AND EVALUATION OF A VIRTUAL REALITY APPLICATION FOR EVALUATION OF A VIRTUAL REALITY APPLICATION FOR COLLABORATIVE PROTOTYPING COLLABORATIVE PROTOTYPING

Design Thinking (DT) is a widely used approach to develop human-centric solutions in organizational settings. One of the main activities within DT is prototyping, which allows for visualizing design ideas. However, the geographical distribution of teams and the lack of suitable working environments challenge these practices. This paper presents a design science research project that resolves these issues through virtual reality. Drawing on findings for creativity support, we derive meta-requirements and design principles and develop the DTinVR application that allows teams to visualize their ideas based on gestural interaction. To the best of our knowledge, we are the first to enable collaborative prototyping using hand tracking. We confirm the effectiveness and positive usability of DTinVR by means of three evaluations and discuss how our design principles can help to develop immersive solutions. The findings of this study contribute to the design knowledge on immersive applications in the information systems discipline
Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.
ESG Tracker: Unbiased and Explainable ESG Profile from Real-time Data

Environmental, social, and governance (ESG) criteria are a set of standards for a company's operations that investors who are conscious about sustainability use to screen potential investments. Many sustainable investors use ESG criteria ratings provided by ESG rating providers, which are mainly based on corporate disclosures. However, the source of data and the diversity of their assessment methodologies have posed several challenges that lead to biased and non-transparent results. We propose a system that provides an unbiased ESG profile for companies from real-time data collected from the dynamic Web (online News and Social media platforms) that is explainable.
TOWARD UNDERSTANDING THE COMPLEXITY OF BUSINESS TOWARD UNDERSTANDING THE COMPLEXITY OF BUSINESS MODELS – A TAXONOMY OF BUSINESS MODEL DEPENDENCIES MODELS – A TAXONOMY OF BUSINESS MODEL DEPENDENCIES

Digital innovation accentuates the importance of technical and economic dependencies existing within and between business models. Companies such as Amazon, Facebook, and Salesforce are known for creating ecosystems in which business model dependencies are a crucial part of success. However, despite the steadily growing interest in business models in various disciplines, including management and information systems, there is no consensus to date about the constituent elements of business model dependencies. Current business model designers and users of modeling languages fail to comprehensively understand such dependencies. To address this gap, we investigate business model dependencies by developing a taxonomy which identifies common characteristics on the basis of a literature review, fourteen business model modeling languages and 95 real-world examples. The resulting taxonomy, consisting of thirteen dimensions, provides a framework for empirical validation, and contributes to research and progress on business model modeling languages.
A Distributed E-Cross Learning Algorithm for Intelligent Multiple Network Slice Selection

Recently, some technological issues in network slicing have been explored. However, most works focus on the physical resource management in this research field and less on slice selection. Different from the existing studies, we explore the problem of intelligent multiple slice selection, which makes some effort to dynamically obtain better user experience in a changeable state. Herein, we consider two factors about user experience: its throughput and energy consumption. Accordingly, a distributed E-cross learning algorithm is developed in the multiagent system where each terminal is regarded as an agent in the distributed network. Furthermore, its convergence is theoretically proven for the dynamic game model. In addition, the complexity of the proposed algorithm is discussed. A mass of simulation results are presented for the convergence and effectiveness of the proposed distributed learning algorithm. Compared with greedy algorithm, the proposed intelligent algorithm has a faster convergence speed. Besides, better user experience is attained effectively with multiple slice access.