IT Portfolio Selection and IT Synergy

This paper proposes the framework of IT portfolio selection and investigates the impact of IT synergy on a firm’s IT portfolio selection. IT resources can be distinguished from other forms of resources by their great potential of enhancing synergy between IT units. Based on prior discussion on types of IT synergy, we classify IT synergy into the three types and examine the effects of the different types of IT synergy on the IT portfolio selection. We found that firms of moderate and high risk tolerance are likely to obtain superior IT portfolio options by enhancing IT synergy, whereas firms of low risk tolerance may not benefit from enhancement of IT synergy

Balancing the Strategic Value and the Operational Value in IT Portfolio Selection

This paper provides a methodological framework in which the problem of IT project portfolio selection is solved by balancing between operational and strategic IT investments. From interviews with CIOs, we found that the interest in IT projects in many firms tends to center on operational benefits, rather than strategic benefits. We examined the impact of strategic IT investments from an IT portfolio perspective. We applied an optimization model for the IT the portfolio selection and developed a computational method to explore the impact of strategic IT investments. We found that, based on a longterm evaluation, IT portfolio selection focusing on both the operational value and the strategic value resulted in greater performance than IT portfolio selection focusing on the operational value alone. In addition, we found that the risk level of the strategic value of IT did not significantly affect the outcome of the IT portfolio in a strategic IT investment

The Acquisitions of Information Technology Firms by M&A Intents: An Empirical Analysis

Over the last decades, a large number of firms have undertaken mergers and acquisitions (M&As) to create synergies through increased production efficiency, increased market power, and quality improvements. Moreover, we have also recently witnessed that an increasing number of firms acquire information technology (IT) firms to create synergies from the customer side as well as the production side. In this study, we examine the post-merger risk of the acquiring firm, measured as its return volatility when IT firms are acquired, and seek to understand the dynamics surrounding M&A transactions. We also identify the conditions under which acquiring firms can mitigate the risks resulting from M&A transactions. We find that a strong run-up in risk occurs before M&A transactions are initiated, but this risk begins to decline over the post-merger years. However, we expect that post-merger risks tend to persist when firms seek M&A transactions with a customer-side motive, whereas this does not occur with a production-side motive. Moreover, we expect to find the conditions under which a firm can mitigate risk from the acquisition of IT firms contingent on its M&A motives

A Mythic Belief Regarding Trust in Artificial Intelligence: Uncovering the Role of Responsibility Perception for AI Use in Decision Makings

This study aims to analyze a mechanism of AI responsibility based on attribution theory. It also identifies a new concept, AI locus of control (AI-LOC), reflecting an individual’s belief about the degree to which AI determines decision performance. To this end, we built a website with embedded AI systems where participants longitudinally made corporate credit rating decisions. We created a dynamic panel dataset that includes participants’ decisions per task and decision performance and attitudes per session. The results revealed that AI-LOC and trust in AI were developed in parallel yet differed over time. AI-LOC positively influenced AI use, but trust in AI did not. We reasoned that individuals would likely exhibit self-serving biases and take an egocentric and disengagement coping strategy regarding their decision-making with AI. This study can contribute to understanding the psychological and behavioral aspects of AI use

Need for Speed in the Sharing Economy: How IT capability drives Innovation Speed?

Though innovation is considered to be the lifeblood of business, speed of innovation is more critical than innovation itself. IT plays a critical role in the process of open innovation as it is based upon collaborating with suppliers and customers. IT enables increased collaboration and generation of insights across the firm’s partner network. We examine the role of IT-enabled capabilities in determining the speed of innovation. We hypothesize that collaboration with customers is more effective than collaboration with suppliers for firms to speedily innovate. Further, a firm’s digital collaboration with customers is more effective when Business Intelligence systems are used. Econometric analyses of data from 249 U.S. firms yields strong support for our hypotheses. While both customer-side and supplier- side digital collaboration are positively associated with innovation speed, the effect of customer-side digital collaboration on innovation speed is stronger. Furthermore, Business Intelligence systems use amplifies the effect of customer-side digital collaboration

When Can AI Reduce Individuals’ Anchoring Bias and Enhance Decision Accuracy? Evidence from Multiple Longitudinal Experiments

This study aimed to identify and explain the mechanism underlying decision-making behaviors adaptive to AI advice. We develop a new theoretical framework by drawing on the anchoring effect and the literature on experiential learning. We focus on two factors: (1) the difference between individuals’ initial estimates and AI advice and (2) the existence of a second anchor (i.e., previous-year credit scores). We conducted two longitudinal experiments in the corporate credit rating context, where correct answers exist stochastically. We found that individuals exhibit some paradoxical behaviors. With greater differences and no second anchor, individuals are more likely to make adjustment efforts, but their initial estimates remain strong anchors. Yet, in multiple-anchor contexts individuals tend to diminish dependence on their initial estimates. We also found that the accuracy of individuals was dependent on their debiasing efforts

Direct synthesis and chemical vapor deposition of 2D carbide and nitride MXenes

Two-dimensional (2D) transition metal carbides and nitrides (MXenes) are a large family of materials actively studied for various applications, especially in the field of energy storage. MXenes are commonly synthesized by etching the layered ternary compounds, MAX phases. We demonstrate a direct synthetic route for scalable and atom-economic synthesis of MXenes, including phases that have not been synthesized from MAX phases, by the reactions of metals and metal halides with graphite, methane, or nitrogen. The direct synthesis enables chemical vapor deposition (CVD) growth of MXene carpets and complex spherulite-like morphologies that form through buckling and release of MXene carpet to expose fresh surface for further reaction. The directly synthesized MXenes showed excellent energy storage capacity for Li-ion intercalation.Comment: 9 pages, 4 figure

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049