Product Differentiation in IT Industries: The Interplay between M&As and In-House Innovation and Exploitative Innovation Orientation

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Qi, Kangkang; Han, Sumin; Jeong, Yeasung; Rhamati, Pouya; Susarla, Anjana; Sambamurthy, Vallabh

Abstract: Firms in IT industries are under constant competitive pressure from incumbent f ... Expand

Abstract: Firms in IT industries are under constant competitive pressure from incumbent firms and startups to offer innovative products and services. This study explores the effect of mergers and acquisitions (M&As) on firms' product differentiation in IT industries. The unique characteristics of IT industries, namely hypercompetition, distributed innovation, modularity of products, and the tacit nature of knowledge, make M&As an attractive mechanism to enable IT firms to stay competitive. Drawing on product differentiation theory, we hypothesize a positive effect of M&As on product differentiation and suggest that this relationship is more potent in firms with higher in-house innovation, an exploitative innovation orientation, and under high levels of industry turbulence. We test our hypotheses using M&A transactions from 1996 to 2016 and a unique 10-K textual analysis-based measure of product differentiation. Using the difference-in-differences identification strategy, the following findings are supported: (1) M&As lead to higher product differentiation, (2) in-house innovation capability and exploitation-oriented innovation strategy positively moderate the positive effect of M&As. These results, which are robust to endogeneities and other robustness tests, contribute to theory and managerial practice by shedding light on the role of M&As as an essential mechanism for product innovation and the complementary role of in-house innovation capability and strategy for firms that intend to take advantage of M&As for creating a competitive edge through differentiating their products and services from competitors. These findings have important managerial value, providing an alternative narrative to the current view on M&As in IT industries as a means of absorbing and dismantling the competition. served or serves on the editorial boards of Information Systems Research and MIS Quarterly. Her research has been funded by prestigious organizations such as the National Institute of Health (NIH).Vallabh "Samba" Sambamurthy is the Albert O. Nicholas Dean of the Wisconsin School of Business at the University of Wisconsin. He is a leading expert on how businesses compete in the digital economy. He has published his work in several leading journals and served on the editorial boards of premier academic journals, including two terms as editor-in-chief of Information Systems Research, and has held leadership roles in professional associations. Sambamurthy has garnered several recognitions for contributions to teaching, research, outreach, and mentoring. Collapse

Topics: product differentiation innovation management database system environmental uncertainty research and development

Methods: robustness check longitudinal research statistical hypothesis test difference in differences propensity score matching

Perceived Fairness of Human Managers Compared with Artificial Intelligence in Employee Performance Evaluation

2023 | Journal of Management Information Systems | Citations: 0

Authors: Qin, Shaojun; Jia, Nan; Luo, Xueming; Liao, Chengcheng; Huang, Ziyao

Abstract: Human managers are increasingly challenged by artificial intelligence (AI) techn ... Expand

Abstract: Human managers are increasingly challenged by artificial intelligence (AI) technologies in performing managerial functions. We undertook a field experiment that used AI vis-à-vis human managers to perform structured, data-intensive evaluations of employee performance. We generate two sets of insights. First, employees considered AI to be both fairer and more accurate in evaluating their performance than the average human manager. Second, to catch up with AI, human managers' fairness perceived by employees played a first-order role by (a) helping human managers, to a greater extent than those managers' evaluation accuracy, to close the performance gap of the employees evaluated by them compared with that of those evaluated by AI, and (b) constraining the effect of human managers' perceived accuracy of evaluations on employees' performance. Thus, facing the competition from AI, it is all the more important for human managers to treat employees fairly and build positive interpersonal relationships with employees. Collapse

Topics: artificial intelligence individual productivity implicit knowledge electronic finance organization learning

Methods: experiment survey field experiment computational algorithm survey design

Theories: organizational theory behavioral theory

Achieving Strategic IT Alignment in Dynamic Environments: On the Value of Loose and Tight Coupling

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Queiroz, Magno; Tallon, Paul; Coltman, Tim

Abstract: Executives continue to grapple with a variety of issues involving strategic IT ... Expand

Abstract: Executives continue to grapple with a variety of issues involving strategic IT alignment (SITA) and adaptation to unexpected market events. For many firms, the rise of global competition and increasing volatility have frustrated efforts to pursue SITA and with it, efforts to achieve organizational agility. In the face of a volatile environment, aligning IT to a changing business strategy can be difficult if IT is slow to respond-any ensuing rigidity traps could hinder agility. This paper introduces a model of SITA based on the concept of loose coupling. In focusing on the form of SITA based on loose or tight coupling between IT strategy and business strategy, we look beyond performance impacts of SITA to investigate how SITA is shaped by environmental change and the resulting implications to organizational agility and performance. Propositions are introduced to help guide future research on SITA in dynamic environments. Collapse

Topics: information systems strategy strategic management

Understanding the Effectiveness of Interactive Elements in Video-Based Learning: An Experimental Study

2023 | International Conference on Business Informatics | Citations: 0

Authors: Raab, Maximilian; Weidinger, Julian; Hirschlein, Nico; Meckenstock, Jan-Niklas; Thron, Lisa; Ulbricht, Eric Felix

Abstract: Video-based learning (VBL) depicts a key element of contemporary educational pr ... Expand

Abstract: Video-based learning (VBL) depicts a key element of contemporary educational practice. While benefits have been identified in research and application, the lack of interactivity that can imply detrimental effects on the resulting learning performance in VBL should not be neglected. As a countermeasure, a plethora of interactive elements embedded in educational videos has emerged. By now, it remains unexplored how different interactive elements compare to one another regarding their effects on learning performance. To address this research gap, we conducted an experimental study in an undergraduate university course and investigated the respective effects of three interactive element types under similar conditions in a VBL environment. Our results offer indications that the specific addition of quizzes and annotations can imply significant improvements of learning performance, while navigation features possess no stimulation. Our work thereby provides educational practice and academia with new perspectives on interactive elements in VBL to improve their utilization. Collapse

Methods: experiment parametric test Student's t-test experimental group ANOVA

HUMAN-AI COLLABORATION IN ORGANISATIONS: A LITERATURE REVIEW ON ENABLING VALUE CREATION

2023 | European Conference On Information Systems | Citations: 0

Authors: Raftopoulos, Marigo; Hamari, Juho

Abstract: ... Expand

Abstract: Collapse

Topics: artificial intelligence value creation systems development decision making database system

Methods: literature study qualitative content analysis machine learning literature sample structured literature research

Theories: socio technical theory dynamic capabilities theory sociotechnical systems theory

Towards Adversarially Superior Malware Detection Models: An Adversary Aware Proactive Approach using Adversarial Attacks and Defenses

2023 | Information Systems Frontiers | Citations: 1

Authors: Rathore, Hemant; Samavedhi, Adithya; Sahay, Sanjay K.; Sewak, Mohit

Abstract: The android ecosystem (smartphones, tablets, etc.) has grown manifold in the las ... Expand

Abstract: The android ecosystem (smartphones, tablets, etc.) has grown manifold in the last decade. However, the exponential surge of android malware is threatening the ecosystem. Literature suggests that android malware can be detected using machine and deep learning classifiers; however, these detection models might be vulnerable to adversarial attacks. This work investigates the adversarial robustness of twenty-four diverse malware detection models developed using two features and twelve learning algorithms across four categories (machine learning, bagging classifiers, boosting classifiers, and neural network). We stepped into the adversary’s shoes and proposed two false-negative evasion attacks, namely GradAA and GreedAA, to expose vulnerabilities in the above detection models. The evasion attack agents transform malware applications into adversarial malware applications by adding minimum noise (maximum five perturbations) while maintaining the modified applications’ structural, syntactic, and behavioral integrity. These adversarial malware applications force misclassifications and are predicted as benign by the detection models. The evasion attacks achieved an average fooling rate of 83.34% (GradAA) and 99.21% (GreedAA) which reduced the average accuracy from 90.35% to 55.22% (GradAA) and 48.29% (GreedAA) in twenty-four detection models. We also proposed two defense strategies, namely Adversarial Retraining and Correlation Distillation Retraining as countermeasures to protect detection models from adversarial attacks. The defense strategies slightly improved the detection accuracy but drastically enhanced the adversarial robustness of detection models. Finally, investigating the robustness of malware detection models against adversarial attacks is an essential step before their real-world deployment and can help in developing adversarially superior detection models. Collapse

Topics: malware Android mobile application database system Google+

Methods: machine learning artificial neural network computational algorithm deep learning descriptive statistic

Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach

2023 | International Conference on Business Informatics | Citations: 0

Authors: Reimann, Raphael; Caron, Matthew

Abstract: The humanitarian crisis resulting from the Russian invasion of Ukraine has led ... Expand

Abstract: The humanitarian crisis resulting from the Russian invasion of Ukraine has led to millions of displaced individuals across Europe. Addressing the evolving needs of these refugees is crucial for hosting countries and humanitarian organizations. This study leverages social media analytics to supplement traditional surveys, providing real-time insights into refugee needs by analyzing over two million messages from Telegram, a vital platform for Ukrainian refugees in Germany. We employ Natural Language Processing techniques, including language identification, sentiment analysis, and topic modeling, to identify well-defined topic clusters such as housing, financial and legal assistance, language courses, job market access, and medical needs. Our findings also reveal changes in topic occurrence and nature over time. To support practitioners, we introduce an interactive web-based dashboard for continuous analysis of refugee needs. Collapse

Topics: Telegram social media digital dashboard Twitter decision making

Methods: topic model natural language processing sentiment analysis transformer model data transformation

From Data Exchanges to Data Markets: An Institutional Perspective

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Reimers, Kai; Guo, Xunhua

Abstract: It becomes increasingly clear that the objectives of privacy and competition po ... Expand

Abstract: It becomes increasingly clear that the objectives of privacy and competition policy are in conflict with one another with regard to platform data. While privacy policies aim at limiting the use of platform data for purposes other than those for which the data were collected in order to protect the privacy of platform users, competition policy aims at making such data widely available in order to curb the power of platforms. We find that current control-and ownership-based approaches are ineffective with regard to their capacity to balance these conflicting objectives and propose an institutional approach which makes platform data saleable. We discuss this approach in view of its capacity to balance the conflicting objectives of privacy and competition policy and with regard to its effectiveness in supporting each separately. Our approach also clarifies the fundamental difference between data markets and other concepts such as data spaces.Network effects are often seen as constituting such entry barriers. However, empirical evidence points to the possibility of effective competition under conditions of network effects where promoters of new From Data Exchanges to Data Markets Collapse

Topics: legal context General Data Protection Regulation privacy data protection network effect

Methods: archival research machine learning structured literature research

Theories: transaction cost economics

On the Economic Nature of Medical Information: Implications for the Development of Information Infrastructures in the Healthcare Sector

2023 | HICSS | Citations: 0

Authors: Reimers, K.; Luo, Y.

Abstract: IT adoption in the healthcare sector still lags behind expectations. The litera ... Expand

Abstract: IT adoption in the healthcare sector still lags behind expectations. The literature has extensively studied IT adoption in the healthcare sector as a problem of information infrastructure development. We extend that literature by adding a supply chain perspective which treats information infrastructures as assemblies of information components. This allows us to apply the theory of collective action to these individual components rather than to the information infrastructure as a whole, as is characteristic of the information infrastructure literature. We find that some information components have the character of common resource pool goods, which require specialized and local institutional arrangements to overcome the implied free-rider problem. This sheds a new light on the current problems of slow IT adoption in the healthcare sector. Collapse

Topics: information technology infrastructure healthcare data supply chain management electronic health record information good

Methods: cross sectional research

Theories: theory of information theory of collective action

Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-Based Textual Data Quality Enrichment

2023 | DESRIST | Citations: 0

Authors: Reinhard, Philipp; Li, Mahei Manhai; Dickhaut, Ernestine; Peters, Christoph; Leimeister, Jan Marco

Abstract: AI-based recommendation systems to augment working conditions in the field of IT ... Expand

Abstract: AI-based recommendation systems to augment working conditions in the field of IT service management (ITSM) have attracted new attention. However, many IT support organizations possess high volumes of tickets but are confronted with low quality, to which they train the underlying models of their AI systems. In particular, support tickets are documented insufficiently due to time pressure and lack of motivation. Following design science research, we design and evaluate an analytics pipeline to address the data quality issue. The pipeline can be applied to assess and extract high-quality support tickets for subsequent model training and operation. Based on a data set of 60.000 real-life support tickets from a manufacturing company, we develop the artifact, instantiate a recommender system and achieve a higher prediction performance in comparison to naïve enrichment methods. In terms of data management literature, we contribute to the understanding of assessing textual ticket data quality. By deriving a pipeline reference model, we move towards a general approach to designing machine learning-driven data quality analytics pipelines for attached recommender systems. Collapse

Topics: data quality recommender system system support shapley additive explanation database system

Methods: machine learning reference modelling design artifact cluster analysis topic model