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Doing good by doing bad
(2022)
This study investigates how tone at the top, implemented by top management, and tone at the bottom, in an employee's immediate work environment, determine noncompliance. We focus on the disallowed actions of employees that improve their own and, in turn, the company's performance, referred to as performance-improving noncompliant behavior (PINC behavior). We conduct a survey of German sales employees to investigate specifically how, on the one hand, (1) corporate rules and (2) performance pressure, both implemented by top management, and, on the other hand, (3) others' PINC expectations and (4) others' PINC behavior, both arising from the employee's immediate work environment, influence PINC behavior. When considered in isolation, we find that corporate rules, as top management's main instrument to guide employee behavior, decrease employee PINC behavior. However, this effect is negatively influenced by the employees' immediate work environment when employees are expected to engage in PINC or when others engage in PINC. In contrast, even though top management places great performance pressure on employees, that by itself does not increase PINC behavior. Overall, our study informs practitioners and researchers about whether and how the four determinants increase or decrease employees' PINC behavior, which is important to comprehend triggers and to counteract such misconduct.
Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively.
Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively.
Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.
Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
Our study applies legitimacy theorizing to service research, zooming in on co-prosumption service business models, which reside on significant direct contacts among provider-actors and customers as well as fellow customers in the service space. Our findings are based on a longitudinal flexible pattern matching method on 17 coworking spaces. The service cocreation nuances the double role of customers as evaluators and cocreators of legitimacy. This is because customers can have immediate perceptions of the actions and values of the services in their legitimacy evaluation while cocreating the service. Legitimacy shaped via social and recursive processes occurs in three stages: provisional, calibrated, and affirmed legitimacy. Findings inform four trajectory mechanisms of value-in-use pattern provenance, emergent Business Model development adaptive to the spatial context and loyal customers, visible trances as well as inside-out and outside-in identification processes. Further, the processes in the micro-ecosystem of an interstitial service space can develop a superordinate logic which overlays the potentially present coopetive and heterogenous institutional logics and interests of service customers.
The sharing economy gains momentum and develops a major economic impact on traditional markets and firms. However, only rudimentary theoretical and empirical insights exist on how sharing networks, i.e., focal firms, shared goods providers and customers, create and capture value in their sharing-based business models. We conduct a qualitative study to find key differences in sharing-based business models that are decisive for their value configurations. Our results show that (1) customization versus standardization of shared goods and (2) the centralization versus particularization of property rights over the shared goods are two important dimensions to distinguish value configurations. A second, quantitative study confirms the visibility and relevance of these dimensions to customers. We discuss strategic options for focal firms to design value configurations regarding the two dimensions to optimize value creation and value capture in sharing networks. Firms can use this two-dimensional search grid to explore untapped opportunities in the sharing economy.
Purpose – Design thinking has become an omnipresent process to foster innovativeness in various fields. Due to its popularity in both practice and theory, the number of publications has been growing rapidly. The authors aim to develop a research framework that reflects the current state of research and allows for the identification of research gaps.
Design/methodology/approach – The authors conduct a systematic literature review based on 164 scholarly articles on design thinking.
Findings – This study proposes a framework, which identifies individual and organizational context factors, the stages of a typical design thinking process with its underlying principles and tools, and the individual as well as organizational outcomes of a design thinking project.
Originality/value – Whereas previous reviews focused on particular aspects of design thinking, such as its characteristics, the organizational culture as a context factor or its role on new product development, the authors provide a holistic overview of the current state of research.
Entrepreneurial failure
(2022)
Although entrepreneurial failure (EF) is a fairly recent topic in entrepreneurship literature, the number of publications has been growing dynamically and particularly rapidly. Our systematic review maps and integrates the research on EF based on a multi-method approach to give structure and consistency to this fragmented field of research. The results reveal that the field revolves around six thematic clusters of EF: 1) Soft underpinnings of EF, 2) Contextuality of EF, 3) Perception of EF, 4) Two-sided effects of EF, 5) Multi-stage EF effects, and 6) Institutional drivers of EF. An integrative framework of the positive and negative effects of entrepreneurial failure is proposed, and a research agenda is suggested.
Research on corporate entrepreneurship—venturing activities by established corporations—has received increasing scholarly attention. We employ bibliometric methods to analyze the literature on corporate entrepreneurship published over the last five decades. Based on the results of citation and co-citation analyses, we reveal central works in the field and how they are interconnected. We investigate the underlying intellectual structure of the field. Our findings provide evidence of the growing maturity and interdisciplinarity of corporate entrepreneurship and provide insight into research themes. We find that resource-based view and its extensions still remain the predominant theoretical perspectives in the field. Drawing on these findings, we suggest directions for future research.
Long-term value creation is expected not only to be concerned with maximizing shareholder value but also includes the impact on other stakeholders and the environment. Environmental, social, and governance (ESG) issues are therefore gaining increasing importance, in line with the growing demand for corporate sustainability. ESG ratings foster the comparison of companies with respect to their sustainable practices. This study aims to investigate how ESG ratings impact financial performance in the European food industry. Ordinary least squares regression is applied to analyze the relation between ESG ratings and financial performance over a 4-year period from 2017 to 2020. The profitability measures Return on Assets (ROA) and Return on Equity (ROE) are employed as financial performance measures, while ESG ratings are obtained from the database CSRHub. Results show that higher ESG ratings are associated with better financial performance. Although the effect is modest in the present study, the findings support previous results that ESG ratings are positively related to financial performance. Nonetheless, they also highlight that ESG ratings strongly converge to the mean, which depicts the need to reassess whether ESG ratings are able to measure actual ESG behavior.
Strategic social media use positively influences organizational goals such as the long-term accrual of social capital, and thus social media information governance has become an increasingly important organizational objective. It is particularly important for humanitarian nongovernmental organizations (HNGOs), whose work relies on accurate and timely information regarding socially altruistic behavior (donations, volunteerism, etc.). Despite the potential of social media for increasing social capital, tensions in governing social media information across an organization's different operational levels (regional, intermediate, and national) pose a difficult challenge. Prominent governance frameworks offer little guidance, as their focus on control and incremental policymaking is largely incompatible with the processes, roles, standards, and metrics needed for managing self-governing social media. This study offers a notion of dynamic and co-evolutionary process management of multi-level organizations as a means of conceptualizing social media information governance for the accrual of organizational social capital. Based on interviews with members of HNGOs, this study reveals tensions that emerge within eight focus areas of accruing social capital in multi-level organizations, explains how dynamic process management can ease those tensions, and proposes corresponding strategy recommendations.
Enterprise systems have long played an important role in businesses of various sizes. With the increasing complexity of today’s business relationships, pecialized application systems are being used more and more. Moreover, emerging technologies such as artificial intelligence are becoming accessible for enterprise systems. This raises the question of the future role of enterprise systems. This minitrack covers novel ideas that contribute to and shape the future role of enterprise systems with five contributions.
Findings in the extant literature are mixed concerning when and how gender diversity benefits team performance. We develop and test a model that posits that gender-diverse teams outperform gender-homogeneous teams when perceived time pressure is low, whereas the opposite is the case when perceived time pressure is high. Drawing on the categorization-elaboration model (CEM; van Knippenberg, De Dreu, & Homan, 2004), we begin with the assumption that information elaboration is the process whereby gender diversity fosters positive effects on team performance. However, also in line with the CEM, we argue that this process can be disrupted by adverse team dynamics. Specifically, we argue that as time pressure increases, higher gender diversity leads to more team withdrawal, which, in turn, moderates the positive indirect effect of gender diversity on team performance via information elaboration such that this effect becomes weaker as team withdrawal increases. In an experimental study of 142 four-person teams, we found support for this model that explains why perceived time pressure affects the performance of gender-diverse teams more negatively than that of gender-homogeneous teams. Our study sheds new light on when and how gender diversity can become either an asset or a liability for team performance.
Entrepreneurship education (EE) has attracted much scholarly attention, showing exponential growth in publication and citation numbers. The research field has become broad, complex, and fragmented, making it increasingly difficult to oversee. Our research goal is to organise and integrate the previous literature. To this end, we use bibliometric analyses, differing from prior analyses, which are outdated or have a different focus. Our results show an immense growth in publications and citations over the last decade and an almost equal involvement of business and educational research. We identify the most productive and influential journals and authors. Our co-citation analysis reveals two research clusters, one focusing on psychological constructs relating to EE, and the other on entrepreneurial behaviour and new venture creation. Based on a review of the 25 most-cited articles on an annual basis, we identify and quantify the most relevant research themes and integrate them into a research framework that we propose for future research. A major finding is that extant research centres around the outcomes of entrepreneurship education, whereas its pedagogy is still mainly a black box.
Since the beginning of the recent global refugee crisis, researchers have been tackling many of its associated aspects, investigating how we can help to alleviate this crisis, in particular, using ICTs capabilities. In our research, we investigated the use of ICT solutions by refugees to foster the social inclusion process in the host community. To tackle this topic, we conducted thirteen interviews with Syrian refugees in Germany. Our findings reveal different ICT usages by refugees and how these contribute to feeling empowered. Moreover, we show the sources of empowerment for refugees that are gained by ICT use. Finally, we identified the two types of social inclusion benefits that were derived from empowerment sources. Our results provide practical implications to different stakeholders and decision-makers on how ICT usage can empower refugees, which can foster the social inclusion of refugees, and what should be considered to support them in their integration effort.
Since the beginning of the recent global refugee crisis, researchers have been tackling many of its associated aspects, investigating how we can help to alleviate this crisis, in particular, using ICTs capabilities. In our research, we investigated the use of ICT solutions by refugees to foster the social inclusion process in the host community. To tackle this topic, we conducted thirteen interviews with Syrian refugees in Germany. Our findings reveal different ICT usages by refugees and how these contribute to feeling empowered. Moreover, we show the sources of empowerment for refugees that are gained by ICT use. Finally, we identified the two types of social inclusion benefits that were derived from empowerment sources. Our results provide practical implications to different stakeholders and decision-makers on how ICT usage can empower refugees, which can foster the social inclusion of refugees, and what should be considered to support them in their integration effort.
With the surging reliance on videoconferencing tools, users may find themselves staring at their reflections for hours a day. We refer to this phenomenon as self-referential information (SRI) consumption and examine its consequences and the mechanism behind them. Building on self-awareness research and the strength model of self-control, we argue that SRI consumption heightens the state of self-awareness and thereby depletes participants’ mental resources, eventually undermining virtual meeting (VM) outcomes. Our findings from a European employee sample revealed contrary effects of SRI consumption across speaker vs listener roles. Engagement with self-view is positively associated with self-awareness, which, in turn, is negatively related to satisfaction with VM process, perceived productivity, and enjoyment. Looking at the self while listening to others exhibits adverse direct and indirect (via self-awareness) effects on VM outcomes. However, looking at the self when speaking exhibits positive direct effects on satisfaction with VM process and enjoyment.
Real options are widely applied in strategic and operational decision-making, allowing for managerial flexibility in uncertaincontexts. Increased scholarly interest has led to an extensive but fragmented research landscape. We aim to measure andsystematize the research field quantitatively. To achieve this goal, we conduct bibliometric performance analyses and bibliographiccoupling analyses with an in-depth content review. The results of the performance analyses show an increasing interest in realoptions since the beginning of the 2000s and identify the most influential journals and authors. The science mappings reveal sixand seven research clusters over the last two decades. Based on an in-depth analysis of their themes, we develop a researchframework comprising antecedents, application areas, internal and external contingencies, and uncertainty resolution throughreal option valuation or reasoning. We identify several gaps in that framework, which we propose to tackle in future research.
Looking for participation
(2022)
A stronger learner orientation through participatory learning increases learning motivation and results. But what does participatory learning mean? Where do learning factories and fabrication laboratories (FabLabs) stand in this context, and how can didactic implementation be improved in this respect? Using a newly developed analytical framework, which contains elements of the stage model of participation and general media didactics, we compare a FabLab and a learning factory example concerning the degree of participation. From this, we derive guidelines for designing participative teaching and learning processes in learning factories. We explain how FabLabs can be an inspiration for the didactic design of learning factories.
An increasing number of clinicians (i.e., nurses and physicians) suffer from mental health-related issues like depression and burnout. These, in turn, stress communication, collaboration, and decision- making—areas in which Conversational Agents (CAs) have shown to be useful. Thus, in this work, we followed a mixed-method approach and systematically analysed the literature on factors affecting the well-being of clinicians and CAs’ potential to improve said well-being by relieving support in communication, collaboration, and decision-making in hospitals. In this respect, we are guided by Brigham et al. (2018)’s model of factors influencing well-being. Based on an initial number of 840 articles, we further analysed 52 papers in more detail and identified the influences of CAs’ fields of application on external and individual factors affecting clinicians’ well-being. As our second method, we will conduct interviews with clinicians and experts on CAs to verify and extend these influencing factors.
Digital Platforms (DPs) has established themself in recent years as a central concept of the Information Technology Science. Due to the great diversity of digital platform concepts, clear definitions are still required. Furthermore, DPs are subject to dynamic changes from internal and external factors, which pose challenges for digital platform operators, developers and customers. Which current digital platform research directions should be taken to address these challenges remains open so far. The following paper aims to contribute to this by outlining a systematic literature review (SLR) on digital platform concepts in the context of the Industrial Internet of Things (IIoT) for manufacturing companies and provides a basis for (1) a selection of definitions of current digital platform and ecosystem concepts and (2) a selection of current digital platform research directions. These directions are diverted into (a) occurrence of digital platforms, (b) emergence of digital platforms, (c) evaluation of digital platforms, (d) development of digital platforms, and (e) selection of digital platforms.
The business problem of having inefficient processes, imprecise process analyses and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS) and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
Widespread on social networking sites (SNSs), envy has been linked to an array of detrimental outcomes for users’ well-being. While envy has been considered a status-related emotion and is likely to be experienced in response to perceiving another’s higher status, there is a lack of research exploring how status perceptions influence the emergence of envy on SNSs. This is important because SNSs typically quantify social interactions and reach with metrics that indicate users’ relative rank and status in the network. To understand how status perceptions impact SNS users, we introduce a new form of metric-based digital status rooted in SNS metrics that are available and visible on a platform. Drawing on social comparison theory and status literature, we conducted an online experiment to investigate how different forms of status contribute to the proliferation of envy on SNSs. Our findings shed light on how metric-based digital status influences feelings of envy on SNSs. Specifically, we could show that metric-based digital status impacts envy through increasing perceptions of others’ socioeconomic and sociometric statuses. Our study contributes to the growing discourse on the negative outcomes associated with SNS use and its consequences for users and society.
The envy spiral
(2020)
On Social Networking Sites (SNS) users disclose mostly positive and often self-enhancing information. Scholars refer to this phenomenon as the positivity bias in SNS communication (PBSC). However, while theoretical explanations for this phenomenon have been proposed, an empirical proof of these theorized mechanisms is still missing. The project presented in this Research-in-Progress paper aims at explaining the PBSC with the mechanism specified in the self-enhancement envy spiral. Specifically, we hypothesize that feelings of envy drive people to post positive and self-enhancing content on SNS. To test this hypothesis, we developed an experimental design allowing to examine the causal effect of envy on the positivity of users’ subsequently posted content. In a preliminary study, we tested our manipulation of envy and could show its effectiveness in inducing different levels of envy between our groups. Our project will help to broaden the understanding of the complex dynamics of SNS and the potentially adverse driving forces underlying them.
Enhancing economic efficiency in modular production systems through deep reinforcement learning
(2024)
In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios.
As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.
It’s personal
(2021)
The new technologies of the Fourth Industrial Revolution (4IR) are disrupting traditional models of work and learning. While the impact of digitalization on education was already a point of serious deliberation, the COVID-19 pandemic has expedited ongoing transitions. With 90% of the world’s student population having been impacted by national lockdowns—online learning has gone from being a luxury to a necessity, in a context where around 3.6 billion people are offline. As the impacts of the 4IR unfold alongside the current crisis, it is not enough for future policy pathways to prioritize educational attainment in the traditional sense; it is essential to reimagine education itself as well as its delivery entirely. Future policy narratives will need to evaluate the very process of learning and identify the ways in which technology can help reduce existing disparities and enhance digital access, literacy and fluency in a scalable manner. In this context, this chapter analyses the status quo of online learning in India and Germany. Drawing on the experiences of these two economies with distinct trajectories of digitalization, the chapter explores how new technologies intersect with traditional education and local sociocultural conditions. Further, the limitations and opportunities presented by dominant ed-tech models is critically analyzed against the ongoing COVID-19 pandemic.
We and AI
(2021)
From learners to educators
(2020)
The rapid growth of technology and its evolving potential to support the transformation of teaching and learning in post-secondary institutions is a major challenge to the basic understanding of both the university and the communities it serves. In higher education, the standard forms of learning and teaching are increasingly being challenged and a more comprehensive process of differentiation is taking place. Student-centered teaching methods are becoming increasingly important in course design and the role of the lecturer is changing from the knowledge mediator to moderator and learning companion. However, this is accelerating the need for strategically planned faculty support and a reassessment of the role of teaching and learning. Even though the benefits of experience-based learning approaches for the development of life skills are well known, most knowledge transfer is still realized through lectures in higher education. Teachers have the goal to design the curriculum, new assignments, and share insights into evolving pedagogy. Student engagement could be the most important factor in the learning success of university students, regardless of the university program or teaching format. Against this background, this article presents the development, application, and initial findings of an innovative learning concept. In this concept, students are allowed to deal with a scientific topic, but instead of a presentation and a written elaboration, their examination consists of developing an online course in terms of content, didactics, and concept to implement it in a learning environment, which is state of the art. The online courses include both self-created teaching material and interactive tasks. The courses are created to be available to other students as learning material after a review process and are thus incorporated into the curriculum.
This study is dedicated to the interdependencies between digital sovereignty and sustainable digitalization, which need to be explicitly linked to an increasing degree in political discourse, academia, and societal debates. Digital skills are the prerequisites for shaping digitalization in the interest of society and sustainable development.
The management of knowledge in organizations considers both established long-term
processes and cooperation in agile project teams. Since knowledge can be both tacit and explicit, its transfer from the individual to the organizational knowledge base poses a challenge in organizations. This challenge increases when the fluctuation of knowledge carriers is exceptionally high. Especially in large projects in which external consultants are involved, there is a risk that critical, company-relevant knowledge generated in the project will leave the company with the external knowledge carrier and thus be lost. In this paper, we show the advantages of an early warning system for knowledge management to avoid this loss. In particular, the potential of visual analytics in the context of knowledge management systems is presented and discussed. We present a project for the development of a business-critical software system and discuss the first implementations and results.
Intrinsic motivation is widely considered essential to creativity because it facilitates more divergent thinking during problem solving. However, we argue that intrinsic motivation has been theorized too heavily as a unitary construct, overlooking various internal factors of a task that can shape the baseline level of intrinsic motivation people have for working on the task. Drawing on theories of cognitive styles, we develop a new scale that measures individual preferences for three different creative thinking styles that we call divergent thinking, bricoleurgent thinking, and emergent thinking. Through a multi-study approach consisting of exploratory factor analysis, confirmatory factor analysis, and convergent validity, we provide psychometric evidence showing that people can have distinct preferences for each cognitive process when generating ideas. Furthermore, when validating this scale through an experiment, we find that each style becomes more dominant in predicting overall enjoyment, engagement, and creativity based on different underlying structures of a task. Therefore, this paper makes both theoretical and empirical contributions to literature by unpacking intrinsic motivation, showing how the alignment between different creative thinking styles and task can be essential to predicting intrinsic motivation, thus reversing the direction of causality between the motivational and cognitive components of creativity typically assumed in literature.
Worth the pain?
(2021)
How do exporting firms react to sanctions? Specifically, which firms are willing — or capable — to serve the market of a sanctioned country? We investigate this question for four sanctions episodes using monthly data on the universe of French exporting firms. We draw on recent econometric advances in the estimation of dynamic fixed effects binary choice models. We find that the introduction of new sanctions in Iran and Russia significantly lowered firm-level probabilities of serving these sanctioned markets, while the (temporary) lifting of the U.S. sanctions on Cuba and the removal of sanctions against Myanmar had no or only small trade-inducing effects, respectively. Additionally, the impact of sanctions is very heterogeneous along firm dimensions and by case particularities. Firms that depend more on trade finance instruments are more strongly affected, while prior experience in the sanctioned country considerably softens the blow of sanctions, and firms can be partly immune to the sanctions effect if they are specialized in serving “crisis countries”. Finally, we find suggestive evidence for sanctions avoidance by exporting indirectly via neighboring countries.
Purpose
The purpose of this paper is to investigate how learning solely via an assistance system influences work performance compared with learning with a combination of an assistance system and additional training. While the training literature has widely emphasised the positive role of on-the-job training, particularly for groups that are often underrepresented in formalised learning situations, organisational studies have stressed the risks that emerge when holistic process knowledge is lacking and how this negatively affects work performance. This study aims at testing these negative effects within an experimental design.
Design/methodology/approach
This paper uses a laboratory experimental design to investigate how assistance-system-guided learning influences the individuals’ work performance and work satisfaction compared with assistance-system-guided learning combined with theoretical learning of holistic process knowledge. Subjects were divided into two groups and assigned to two different settings. In the first setting, the participants used the assistance systems as an orientation and support tool right at the beginning and learned the production steps exclusively in this way. In the second setting, subjects received an additional 10-min introduction (treatment) at the beginning of the experiment, including detailed information regarding the entire work process.
Findings
This study provides evidence that learners provided with prior process knowledge achieve a better understanding of the work process leading to higher levels of productivity, quality and work satisfaction. At the same time, the authors found evidence for differences among workers’ ability to process and apply this additional information. Subjects with lower productivity levels faced more difficulties processing and applying additional process information.
Research limitations/implications
Methodologically, this study goes beyond existing research on assistance systems by using a laboratory experimental design. Though the external validity of this method is limited by the artificial setting, it is a solid way of studying the impact of different usages of digital assistance systems in terms of training. Further research is required, however, including laboratory experiments with larger case numbers, company-level case studies and analyses of survey data, to further confirm the external validity of the findings of this study for the workplace.
Practical implications
This study provides some first evidence that holistic process knowledge, even in low-skill tasks, has an added value for the production process. This study contributes to firms' training policies by exploring new, digitalised ways of guided on-the-job training and demonstrates possible training benefits for people with lower levels of (initial) abilities and motivation.
Social implications
This study indicates the advantage for companies and societies to invest in additional skills and training and points at the limitations of assistance systems. This paper also contributes to training policies by exploring new, digitalised ways of guided on-the-job training and demonstrates possible training benefits for people with lower levels of (initial) abilities and motivation.
Originality/value
This study extends existing research on digital assistance systems by investigating their role in job-related-training. This paper contributes to labour sociology and organisational research by confirming the importance of holistic process knowledge as opposed to a solely task-oriented digital introduction.
We welcome you to the 53rd Hawaii International Conference on System Sciences (HICSS) conference. After joining with Intentional Forgetting Minitrack last year, this is the fourth year of the Organizational Learning Minitrack. We add Unlearning, and Intentional Forgetting to proudly bring you the latest research focused on organizational learning issues within the Knowledge Innovation and Entrepreneurial Systems Track. The ability to update, change and use current knowledge effectively, especially in light of the ongoing knowledge explosion, can be costly for any organization. Organizations that consider themselves “learning” or “knowledge-based” organizations must develop a competent workforce using KM strategies. Success in organizations involves developing a variety of human factors for changing competencies. With technological change, modification and revisions, many skills require updating for a competitive advantage in the marketplace. The focus on new techniques and insights into how individuals and organizations use their knowledge is our focus for the improvement of organizational learning in this Minitrack.
We welcome you to the 54th Hawaii International Conference on System Sciences (HICSS-54) conference. This is the fifth year for the Organizational Learning Minitrack which has had the usual growing pains: two years ago, we added the topic of Unlearning and joined with the Intentional Forgetting Minitrack - as these topics are all organizationally-based knowledge management issues. We proudly bring you the latest research focused on the methods to develop and maintain organizational learning within the Knowledge Innovation and Entrepreneurial Systems Track. The ability to update, change and use current knowledge effectively, especially in light of the ongoing knowledge explosion, can be costly for any organization. Organizations that consider themselves “learning” or “knowledge-based” organizations must develop a competent workforce using KM strategies. Success in organizations involves developing a variety of human factors for changing competencies. With technological change, modification and revisions, many skills require updating for a competitive advantage in the marketplace. The focus on new techniques and insights into how individuals and organizations use their knowledge is our focus for the improvement of organizational
learning in this Minitrack.
Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect.
New technological applications such as Augmented Reality or Massive Open Online Courses (MOOCs) lead to alternative ways of learning. In order to be able to use this to its potential, the promotion of digital competencies “Digital Competence is the set of knowledge, skills, attitudes, abilities, strategies, and awareness that are required when using ICT and digital media to perform tasks; solve problems; communicate; manage information; collaborate; create and share content; and build knowledge effectively, efficiently, appropriately, critically, creatively, autonomously, flexibly, ethically, reflectively for work, le sure, participation, learning, and socialising.” (Ferrari, 2012). and a corresponding amount of practical "learning-by-doing" effects is required (cf. Ecker/Campbell 2019, p. 154). For this purpose, spaces and framework conditions must be created for application-based learning, which is also increasingly required by the employment market. In this context, we take a closer look at a new emerging subculture in university infrastructure called Maker Movement (MM). Our research work aims at investigating the pedagogical potential of particularly university-integrated makerspaces (MS) to enhance experiential learning with digital tools. To decode the innovative potential, we collected qualitative data from nine in-depth, semi-structured interviews with lab managers and researchers at European MS in six different countries.
Research into the effects of social media on well-being often distinguishes “active” and “passive” use, with passive use supposedly more harmful to well-being (i.e., the passive use hypothesis). Recently, several studies and reviews have begun to question this hypothesis and its conceptual basis, the active/passive dichotomy. As this dichotomy has become a staple of social media research but evidence challenging its validity is mounting, a comprehensive debate on its pros, cons, and potential future is needed. This adversarial review brings together two voices – one more supportive, and the other more critical – toward the active/passive model. In constructive dialogue, we summarize and contrast our two opposing positions: The first position argues that the active/passive dichotomy is a useful framework because it adequately describes how and why passive use is (more) harmful for well-being. The second position challenges the validity of the dichotomy and the passive use hypothesis specifically. Arguments are presented alongside (a) the empirical basis, (b) conceptualization, and (c) operationalization of active and passive use, with particular focus on the passive use hypothesis. Rather than offering a conciliatory summary of the status quo, the goal of this review is to carve out key points of friction in the literature on the effects of social media through fruitful debate. We summarize our main agreements and unresolved disagreements on the merits and shortcomings of the active/passive dichotomy. In doing so, this review paves the way for researchers to decide whether and how they want to continue applying this lens in their future work.
Openness indicators for the evaluation of digital platforms between the launch and maturity phase
(2024)
In recent years, the evaluation of digital platforms has become an important focus in the field of information systems science. The identification of influential indicators that drive changes in digital platforms, specifically those related to openness, is still an unresolved issue. This paper addresses the challenge of identifying measurable indicators and characterizing the transition from launch to maturity in digital platforms. It proposes a systematic analytical approach to identify relevant openness indicators for evaluation purposes. The main contributions of this study are the following (1) the development of a comprehensive procedure for analyzing indicators, (2) the categorization of indicators as evaluation metrics within a multidimensional grid-box model, (3) the selection and evaluation of relevant indicators, (4) the identification and assessment of digital platform architectures during the launch-to-maturity transition, and (5) the evaluation of the applicability of the conceptualization and design process for digital platform evaluation.
As the focus on digital transformation and its unexplored opportunities is prospering, consulting firms have also turned their attention to it. Despite this increased attention, digital transformation project failure rate remains high, thereby reaffirming the “IT productivity paradox” or “AI productivity paradox. The purpose of this paper is to holistically scrutinize the current approach of digital transformation consulting. A deductive qualitative study draws upon the Human Technology Performance model to elucidate papers on digital transformation published by four major management consulting firms in 2014-2019. Whereas the focus on customer-centricity and some measures of corporate performance is prominent in the consulting approaches, the study results have revealed several “blind spots” that are still either neglected or poorly covered. Three main blind spots are particularly prominent from the analysis. First of all, consulting firms often apply a “one size fits all” approach, thereby neglecting contextual factors, such as age, size, or industry. Secondly, they neglect process level in the return on investment of IT. Thirdly, the suitability of IS development methods remains poorly considered. Hence, the paper argues that, while consulting firms purport to support digital transformation exigences and efforts, they, in fact, adhere to traditional approaches to business consulting.
The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities. Accordingly, an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods. We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities. This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach, which enables non-computer scientists to code. Therein, the learning content is provided in small tasks and embedded in learning factory scenarios. Therefore, different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated, yet condensed skills catalogue. This enables the development of training and education units that focus on the most important skills demanded on the market. To achieve this objective, individual learning scenarios are developed. Of course, proper basic skills in coding cannot be learned over night but software programming is also no sorcery.
For the last 20 years, enterprise architecture management (EAM) was primarily an instrument for harmonizing and consolidating IT landscapes and is lived as a transformation and governance discipline. It, however, is rather related to IT strategy than aligned to the actual corporate strategy and the work of the enterprise architect is characterized by tasks like prescribing, monitoring, documenting, and controlling. As digital transformation continues apace, companies are facing new challenges that lead to a volatile, uncertain, complex, and ambiguous (VUCA) world. To face these challenges, vision, understanding, clarity and agility allow to anticipative and implement necessary changes. This, of course, has implications for the role of the enterprise architect. S/he needs to start actively supporting innovation and taking more of an advisory role instead of just being driven by the current state of the enterprise architecture. This paper investigates the role of the enterprise architect in the VUCA world. Based on current literature and expert interviews, a survey was conducted among consultants who work as (or with) enterprise architects. Survey results include the evaluation of statements on current tasks of enterprise architects, their influence on projects and companies as well as future requirements on the roles of the enterprise architect. The results from the survey were synthesized with the findings from literature to derive the roles, tasks and skills of enterprise architect in the VUCA world.
This study utilizes cross-country survey data to analyze differences in attitudes toward cryptocurrency as an alternative to traditional money issued by a central bank. Particularly, we investigate women’s general attitude toward cryptocurrency systems. Results suggest that women invest less into cryptocurrency, show less interest in the future cryptocurrency investment, and see less economic potential in these systems than men do. Further evidence shows that these attitudes are directly connected with lower literacy in cryptocurrency systems. These findings support theory on gender differences in investment behavior. We contribute to the existing literature by conducting a cross-country survey on cryptocurrency attitudes in Europe and Asia, and hence show that this gender effect is robust across these cultures.
Does AI control or support?
(2022)
Many companies are currently investing in artificial intelligence (AI) because of its potential to increase customer satisfaction or financial performance. However, the success rates in implementing AI systems are low, partly due to technology-centric approaches that neglect work practices. This study draws on Bourdieu’s theory of practice to highlight the potential power shift related to AI in customer relationship management, based on the concepts field, capital, and habitus. Two longitudinal case studies were conducted to understand the power shift related to AI implementation. These two AI systems were designed with the objective to support employees. However, subsequently, their implementation changed the balance of power with a significant shift towards more management control, resulting in a devaluation of employees’ work practices. The paper discusses implications for theory and practice in terms of the discrepancies and power shifts following the introduction of AI systems to support customer relationship management.
While a growing body of literature finds positive impacts of Start-Up Subsidies (SUS) on labor market outcomes of participants, little is known about how the design of these programs shapes their effectiveness and hence how to improve policy. As experimental variation in program design is unavailable, we exploit the 2011 reform of the current German SUS program for the unemployed which strengthened caseworkers' discretionary power, increased entry requirements and reduced monetary support. We estimate the impact of the reform on the program's effectiveness using samples of participants and non-participants from before and after the reform. To control for time-constant unobserved heterogeneity as well as differential selection patterns based on observable characteristics over time, we combine Difference-in-Differences with inverse probability weighting using covariate balancing propensity scores. Holding participants' observed characteristics as well as macroeconomic conditions constant, the results suggest that the reform was successful in raising employment effects on average. As these findings may be contaminated by changes in selection patterns based on unobserved characteristics, we assess our results using simulation-based sensitivity analyses and find that our estimates are highly robust to changes in unobserved characteristics. Hence, the reform most likely had a positive impact on the effectiveness of the program, suggesting that increasing entry requirements and reducing support increased the program's impacts while reducing the cost per participant. (C) 2021 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.