Measuring Information Technology Payoff: A Meta-Analysis of Structural Variables in Firm-Level Empirical Research

2003 ◽  
Vol 14 (2) ◽  
pp. 127-145 ◽  
Author(s):  
Rajiv Kohli ◽  
Sarv Devaraj
Keyword(s):  
Information Technology ◽  
Empirical Research ◽  
Meta Analysis ◽  
Firm Level ◽  
Structural Variables

scholarly journals The impact of IT investment on firm performance based on MCDM techniques

2019 ◽  
Vol 9 (5) ◽  
pp. 4344
Author(s):  
Ansar Daghouri ◽  
Khalifa Mansouri ◽  
Mohammed Qbadou
Keyword(s):  
Information Technology ◽  
Firm Performance ◽  
Financial Performance ◽  
Meta Analysis ◽  
Recent Past ◽  
Firm Level ◽  
Technology Investment ◽  
Information Technology Investments ◽  
Almost All ◽  
The Impact

In the recent past years, researchers have presented conflicting results regarding the impact of information technology investment on firm performance. Almost all studies on information technology productivity and it role for companies performance are based on data collected and meta-analysis and do not offer a methodology or prototype of analysis in any field This study presents an attempt to adopt a multi-criteria decision making approach to evaluate the non-financial performance of companies using two famous methods. Furthermore, our results try to investigate the effects of information technology investments on firms’ non-financial performance. Finding show that investment in information systems is not necessarily related to achieving a good non-financial performance at the firm level.

Which Data to Meta-Analyze, and How?

2019 ◽  
Vol 227 (1) ◽  
pp. 64-82 ◽  
Author(s):  
Martin Voracek ◽  
Michael Kossmeier ◽  
Ulrich S. Tran
Keyword(s):  
Empirical Research ◽  
Meta Analysis ◽  
Curve Analysis ◽  
Research Progress ◽  
Primary Data ◽  
Statistical Procedures ◽  
Graphical Displays ◽  
Primary Data Analysis ◽  
Meta Analyses ◽  
Data Analytic

Abstract. Which data to analyze, and how, are fundamental questions of all empirical research. As there are always numerous flexibilities in data-analytic decisions (a “garden of forking paths”), this poses perennial problems to all empirical research. Specification-curve analysis and multiverse analysis have recently been proposed as solutions to these issues. Building on the structural analogies between primary data analysis and meta-analysis, we transform and adapt these approaches to the meta-analytic level, in tandem with combinatorial meta-analysis. We explain the rationale of this idea, suggest descriptive and inferential statistical procedures, as well as graphical displays, provide code for meta-analytic practitioners to generate and use these, and present a fully worked real example from digit ratio (2D:4D) research, totaling 1,592 meta-analytic specifications. Specification-curve and multiverse meta-analysis holds promise to resolve conflicting meta-analyses, contested evidence, controversial empirical literatures, and polarized research, and to mitigate the associated detrimental effects of these phenomena on research progress.

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Convergence of firm-level productivity, globalisation and information technology: Evidence from France

2012 ◽  
Vol 116 (2) ◽  
pp. 244-246 ◽  
Author(s):  
Paul-Antoine Chevalier ◽  
Rémy Lecat ◽  
Nicholas Oulton
Keyword(s):  
Information Technology ◽  
Firm Level

Skills, Management of Skills, and IT Skills Requirements

2011 ◽  
pp. 1-25
Author(s):  
Makoto Nakayama ◽  
Norma Sutcliffe
Keyword(s):  
Information Technology ◽  
Information System ◽  
Human Resource ◽  
Effective Management ◽  
Firm Level ◽  
Systems Security ◽  
Key Issues ◽  
It Skills ◽  
Skill Shortages ◽  
It Operations

Information technology (IT) skill shortages appear at the market level occasionally—usually for emerging technologies, unanticipated challenges, and/or unresolved issues such as systems security. Even when a market-level skill shortage does not exist, a firm can still suffer from skill shortages for its critical information system (IS) project and/or IT operations unless the firm plans and manages its needs for IT skills. This chapter first surveys IT skills at the market level and then at the firm level to gain a perspective on the issues. Attention turns to the nature and characteristics of skills in general—not just IT skills—by reviewing past literature. The management of skills is deeply rooted in the management of knowledge, skills, and abilities (KSAs) and human resource practices of the firm. Key issues and lessons are drawn from the literature in those areas. We conclude by considering the nature and characteristics of IT skills in developing an agenda for the effective management of IT skills.

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