@article{53213, author = {{Amiri, Arman and Tavana, Madjid and Arman, Hosein}}, issn = {{2542-6605}}, journal = {{Internet of Things}}, keywords = {{Management of Technology and Innovation, Artificial Intelligence, Computer Science Applications, Hardware and Architecture, Engineering (miscellaneous), Information Systems, Computer Science (miscellaneous), Software}}, publisher = {{Elsevier BV}}, title = {{{An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for Bitcoin Price Prediction}}}, doi = {{10.1016/j.iot.2023.101027}}, volume = {{25}}, year = {{2024}}, } @article{48517, author = {{Hubner-Benz, Sylvia and Baum, Matthias}}, issn = {{1742-5360}}, journal = {{International Journal of Entrepreneurial Venturing}}, keywords = {{Management of Technology and Innovation, Strategy and Management, Business and International Management}}, number = {{1}}, publisher = {{Inderscience Publishers}}, title = {{{What predicts effectuation preferences Disentangling individual and environmental factors and illuminating decision criteria}}}, doi = {{10.1504/ijev.2023.129283}}, volume = {{15}}, year = {{2023}}, } @article{48900, author = {{Diederich, Sarah and Iseke, Anja and Pull, Kerstin and Schneider, Martin}}, issn = {{0958-5192}}, journal = {{The International Journal of Human Resource Management}}, keywords = {{Management of Technology and Innovation, Organizational Behavior and Human Resource Management, Strategy and Management, Business and International Management, Industrial relations}}, pages = {{1--29}}, publisher = {{Informa UK Limited}}, title = {{{Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level}}}, doi = {{10.1080/09585192.2023.2273331}}, year = {{2023}}, } @article{49446, author = {{Diederich, Sarah and Iseke, Anja and Pull, Kerstin and Schneider, Martin}}, issn = {{0958-5192}}, journal = {{The International Journal of Human Resource Management}}, keywords = {{Management of Technology and Innovation, Organizational Behavior and Human Resource Management, Strategy and Management, Business and International Management, Industrial relations}}, pages = {{1--29}}, publisher = {{Informa UK Limited}}, title = {{{Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level}}}, doi = {{10.1080/09585192.2023.2273331}}, year = {{2023}}, } @article{53220, author = {{Tavana, Madjid and Khalili Nasr, Arash and Ahmadabadi, Alireza Barati and Amiri, Alireza Shamekhi and Mina, Hassan}}, issn = {{2542-6605}}, journal = {{Internet of Things}}, keywords = {{Management of Technology and Innovation, Artificial Intelligence, Computer Science Applications, Hardware and Architecture, Engineering (miscellaneous), Information Systems, Computer Science (miscellaneous), Software}}, publisher = {{Elsevier BV}}, title = {{{An interval multi-criteria decision-making model for evaluating blockchain-IoT technology in supply chain networks}}}, doi = {{10.1016/j.iot.2023.100786}}, volume = {{22}}, year = {{2023}}, } @article{53226, author = {{Marín, Raquel and Santos-Arteaga, Francisco J. and Tavana, Madjid and Di Caprio, Debora}}, issn = {{2444-569X}}, journal = {{Journal of Innovation & Knowledge}}, keywords = {{Management of Technology and Innovation, Marketing, Economics and Econometrics, Business and International Management}}, number = {{4}}, publisher = {{Elsevier BV}}, title = {{{Value Chain digitalization and technological development as innovation catalysts in small and medium-sized enterprises}}}, doi = {{10.1016/j.jik.2023.100454}}, volume = {{8}}, year = {{2023}}, } @article{53224, author = {{Santos-Arteaga, Francisco J. and Di Caprio, Debora and Tavana, Madjid}}, issn = {{0040-1625}}, journal = {{Technological Forecasting and Social Change}}, keywords = {{Management of Technology and Innovation, Applied Psychology, Business and International Management}}, publisher = {{Elsevier BV}}, title = {{{A combinatorial data envelopment analysis with uncertain interval data with application to ICT evaluation}}}, doi = {{10.1016/j.techfore.2023.122510}}, volume = {{191}}, year = {{2023}}, } @article{41929, abstract = {{AbstractThe advent of social media and its commodification have created a never-ending feedback loop between businesses and their customers. In this context, constant negative Word-of-Mouth (NWOM) may jeopardize a corporate image and cause defensiveness in corporate communication. This paper presents a case study of several customer service accounts of the railway company Deutsche Bahn on Twitter to investigate the management and control of constant NWOM and the impact of accountability strategies on customers’ perception of the firm. To this end, a sample of 36,757 Twitter postings was drawn and analyzed by means of sentiment and content analysis techniques. The findings suggest that the perceived accountability towards the firm declined in case of an attitude shift towards the user. In contrast, the firm was being held accountable more insistently after expressed defensiveness, regardless of the firm’s actual accountableness. With this paper, we introduce the notion of accountability management and an accompanying theoretical framework to the literature. This provides a novel perspective on constant NWOM countermeasures for organizations that are part of ‘toxic’ industries or face unrightfully claimed accusations, i.e., when being held accountable for outer circumstances beyond their control.}}, author = {{Mirbabaie, Milad and Stieglitz, Stefan and Marx, Julian}}, issn = {{2366-6153}}, journal = {{Schmalenbach Journal of Business Research}}, keywords = {{Management of Technology and Innovation, General Economics, Econometrics and Finance, General Business, Management and Accounting}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Negative Word of Mouth On Social Media: A Case Study of Deutsche Bahn’s Accountability Management}}}, doi = {{10.1007/s41471-022-00152-w}}, year = {{2023}}, } @article{35740, abstract = {{AbstractWhile the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.}}, author = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}}, issn = {{1019-6781}}, journal = {{Electronic Markets}}, keywords = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}}, number = {{1}}, pages = {{375--396}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}}, doi = {{10.1007/s12525-022-00530-6}}, volume = {{32}}, year = {{2022}}, } @article{50463, abstract = {{PurposeEnabled by increased (“big”) data stocks and advanced (“machine learning”) analyses, the concept of human resource analytics (HRA) is expected to systematically improve decisions in human resource management (HRM). Since so far empirical evidence on this is, however, lacking, the authors' study examines which combinations of data and analyses are employed and which combinations deliver on the promise of improved decision quality.Design/methodology/approachTheoretically, the paper employs a neo-configurational approach for founding and conceptualizing HRA. Methodically, based on a sample of German organizations, two varieties (crisp set and multi-value) of qualitative comparative analysis (QCA) are employed to identify combinations of data and analyses sufficient and necessary for HRA success.FindingsThe authors' study identifies existing configurations of data and analyses in HRM and uncovers which of these configurations cause improved decision quality. By evidencing that and which combinations of data and analyses conjuncturally cause decision quality, the authors' study provides a first confirmation of HRA success.Research limitations/implicationsMajor limitations refer to the cross-sectional and national sample and the usage of subjective measures. Major implications are the suitability of neo-configurational approaches for future research on HRA, while deeper conceptualizing and researching both the characteristics and outcomes of HRA constitutes a core future task.Originality/valueThe authors' paper employs an innovative theoretical-methodical approach to explain and analyze conditions that conjuncturally cause decision quality therewith offering much needed empirical evidence on HRA success.}}, author = {{Strohmeier, Stefan and Collet, Julian and Kabst, Rüdiger}}, issn = {{1746-5265}}, journal = {{Baltic Journal of Management}}, keywords = {{Management of Technology and Innovation, Marketing, Organizational Behavior and Human Resource Management, Strategy and Management, Business and International Management}}, number = {{3}}, pages = {{285--303}}, publisher = {{Emerald}}, title = {{{(How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis}}}, doi = {{10.1108/bjm-05-2021-0188}}, volume = {{17}}, year = {{2022}}, } @article{37138, abstract = {{AbstractAssuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for (e.g., by providing high quality training data), employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection processes more objective. However, as the final hiring decision is likely to remain with humans, prevalent human biases could still cause discrimination. This work investigates the impact of an AI-based system’s candidate recommendations on humans’ hiring decisions and how this relation could be moderated by an Explainable AI (XAI) approach. We used a self-developed platform and conducted an online experiment with 194 participants. Our quantitative and qualitative findings suggest that the recommendations of an AI-based system can reduce discrimination against older and female candidates but appear to cause fewer selections of foreign-race candidates. Contrary to our expectations, the same XAI approach moderated these effects differently depending on the context.}}, author = {{Hofeditz, Lennart and Clausen, Sünje and Rieß, Alexander and Mirbabaie, Milad and Stieglitz, Stefan}}, issn = {{1019-6781}}, journal = {{Electronic Markets (ELMA)}}, keywords = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring}}}, doi = {{10.1007/s12525-022-00600-9}}, year = {{2022}}, } @article{46634, author = {{Alavi, Sascha and Böhm, Eva and Habel, Johannes and Wieseke, Jan and Schmitz, Christian and Brüggemann, Felix}}, issn = {{0737-6782}}, journal = {{Journal of Product Innovation Management}}, keywords = {{Management of Technology and Innovation, Strategy and Management}}, number = {{3}}, pages = {{445--463}}, publisher = {{Wiley}}, title = {{{The ambivalent role of monetary sales incentives in service innovation selling}}}, doi = {{10.1111/jpim.12600}}, volume = {{39}}, year = {{2022}}, } @article{30735, abstract = {{While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.}}, author = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}}, issn = {{1019-6781}}, journal = {{Electronic Markets}}, keywords = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}}, doi = {{10.1007/s12525-022-00530-6}}, year = {{2022}}, } @article{48520, author = {{Hubner-Benz, Sylvia and Rudic, Biljana and Baum, Matthias}}, issn = {{0958-5192}}, journal = {{The International Journal of Human Resource Management}}, keywords = {{Management of Technology and Innovation, Organizational Behavior and Human Resource Management, Strategy and Management, Business and International Management, Industrial relations}}, number = {{11}}, pages = {{2137--2172}}, publisher = {{Informa UK Limited}}, title = {{{How entrepreneur’s leadership behavior and demographics shape applicant attraction to new ventures: the role of stereotypes}}}, doi = {{10.1080/09585192.2021.1893785}}, volume = {{34}}, year = {{2021}}, } @article{52705, author = {{Winkler, Christoph and Fust, Alexander and Jenert, Tobias}}, issn = {{0047-2778}}, journal = {{Journal of Small Business Management}}, keywords = {{Management of Technology and Innovation, Strategy and Management, General Business, Management and Accounting}}, number = {{4}}, pages = {{2071--2096}}, publisher = {{Informa UK Limited}}, title = {{{From entrepreneurial experience to expertise: A self-regulated learning perspective}}}, doi = {{10.1080/00472778.2021.1883041}}, volume = {{61}}, year = {{2021}}, } @article{37144, abstract = {{AbstractArtificial intelligence (AI) is being increasingly integrated into enterprises to foster collaboration within humanmachine teams and assist employees with work-related tasks. However, introducing AI may negatively impact employees’ identifications with their jobs as AI is expected to fundamentally change workplaces and professions, feeding into individuals’ fears of being replaced. To broaden the understanding of the AI identity threat, the findings of this study reveal three central predictors for AI identity threat in the workplace: changes to work, loss of status position, and AI identity predicting AI identity threat in the workplace. This study enriches information systems literature by extending our understanding of collaboration with AI in the workplace to drive future research in this field. Researchers and practitioners understand the implications of employees’ identity when collaborating with AI and comprehend which factors are relevant when introducing AI in the workplace.}}, author = {{Mirbabaie, Milad and Brünker, Felix and Möllmann Frick, Nicholas R. J. and Stieglitz, Stefan}}, issn = {{1019-6781}}, journal = {{Electronic Markets}}, keywords = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}}, number = {{1}}, pages = {{73--99}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{The rise of artificial intelligence – understanding the AI identity threat at the workplace}}}, doi = {{10.1007/s12525-021-00496-x}}, volume = {{32}}, year = {{2021}}, } @article{48524, author = {{Hubner-Benz, Sylvia}}, issn = {{1742-5360}}, journal = {{International Journal of Entrepreneurial Venturing}}, keywords = {{Management of Technology and Innovation, Strategy and Management, Business and International Management}}, number = {{2}}, publisher = {{Inderscience Publishers}}, title = {{{When entrepreneurs become leaders: how entrepreneurs deal with people management}}}, doi = {{10.1504/ijev.2020.105571}}, volume = {{12}}, year = {{2020}}, } @article{48521, author = {{Rudic, Biljana and Hubner-Benz, Sylvia and Baum, Matthias}}, issn = {{2352-6734}}, journal = {{Journal of Business Venturing Insights}}, keywords = {{Management of Technology and Innovation, Business and International Management}}, publisher = {{Elsevier BV}}, title = {{{Hustlers, hipsters and hackers: Potential employees’ stereotypes of entrepreneurial leaders}}}, doi = {{10.1016/j.jbvi.2020.e00220}}, volume = {{15}}, year = {{2020}}, } @article{46636, author = {{Böhm, Eva and Eggert, Andreas and Terho, Harri and Ulaga, Wolfgang and Haas, Alexander}}, issn = {{0885-3134}}, journal = {{Journal of Personal Selling and Sales Management}}, keywords = {{Management of Technology and Innovation, Human Factors and Ergonomics}}, number = {{3}}, pages = {{180--197}}, publisher = {{Informa UK Limited}}, title = {{{Drivers and outcomes of salespersons’ value opportunity recognition competence in solution selling}}}, doi = {{10.1080/08853134.2020.1778484}}, volume = {{40}}, year = {{2020}}, } @article{48526, author = {{Hubner-Benz, Sylvia and Baum, Matthias}}, issn = {{1742-5360}}, journal = {{International Journal of Entrepreneurial Venturing}}, keywords = {{Management of Technology and Innovation, Strategy and Management, Business and International Management}}, number = {{4}}, publisher = {{Inderscience Publishers}}, title = {{{Effectuation, entrepreneurs' leadership behaviour, and employee outcomes: a conceptual model}}}, doi = {{10.1504/ijev.2018.093917}}, volume = {{10}}, year = {{2018}}, }