Robert Neil Killins, David W. Johnk and Peter V. Egly
The purpose of this paper is to explore the impact of financial regulation policy uncertainty (FRPU) on bank profit and risk.
Abstract
Purpose
The purpose of this paper is to explore the impact of financial regulation policy uncertainty (FRPU) on bank profit and risk.
Design/methodology/approach
This study applies dynamic panel techniques and uses the Baker et al. (2016) FRPU index and macroeconomic variables to assess FRPU’s impact on bank profit and risk using Federal Deposit Insurance Corporation call reports from Q1 2000 to Q4 2016 for over 4,760 commercial banks.
Findings
The effect of FRPU on profitability (Return on Assets [ROA] and Return on Equity [ROE]) and risk (standard deviation of ROA and ROE) produces complex results. FRPU negatively (positively) impacts profits for small and large banks (money center banks). There is a positive impact on FRPU for small and medium-sized banks, with no impact reported for the large and money center banks.
Practical implications
Findings lead to several implications for financial services regulators, investors and executives as summarized in the conclusion. It is essential to ensure that clear communication channels are open especially to small and medium-sized banks for proper strategic planning, given their greater sensitivity to regulatory uncertainty.
Originality/value
This paper contributes to the literature as follows. First, it explores the impact of FRPU on bank profits and risk using a novel index introduced by Baker et al. (2016). This news-based continuous measure presents a bank profit modeling approach that differs from traditional event study methodology. Second, a large sample of US commercial banks is used which represents an important departure from banking regulation studies.
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Introduction: Many organisations nowadays use artificial intelligence (AI) in human resource (HR) activities like talent acquisition, onboarding of new employees, learning and…
Abstract
Introduction: Many organisations nowadays use artificial intelligence (AI) in human resource (HR) activities like talent acquisition, onboarding of new employees, learning and development, succession planning, retention of employees, and automation of administrative tasks. When AI is integrated with HR practices, it helps HR personnel to focus more on the strategic aspects of the HR function and relieve them from routine HR activities.
Purpose: The readiness of employees to accept any change depends on organisational facilitation to change, employee willingness to accept the change, the requirement for change, situational factors, etc. This research studies the factors influencing employees’ change readiness towards acceptance of AI in HR practices. The researchers also strive to develop a conceptual technology adoption model for AI in HR practices by studying the earlier models. Finally, the research explores the acceptance of AI by various service sector employees and identifies whether there is any difference in their acceptance of AI based on demographic variables.
Methodology: A conceptual framework was derived using a combination of previous models, including the Technology Readiness Index (TRI), Change Readiness Scale, Technology Acceptance Model (TAM), Technology, Organization, and Environment (TOE) model, and change readiness scale. A structured questionnaire was designed and distributed to 228 respondents from the service sector based on the conceptual framework. An exploratory factor analysis (EFA) was used to determine the elements that influence employees’ level of change readiness.
Findings: The exploratory results on data collected from 228 respondents show that the model can be used for further research if a confirmatory factor analysis and validity and reliability test are performed. Employees are aware of AI and how it is used in HR practices, based on the study results. Moreover, while most respondents favour using AI in their company’s HR practices, they are wary of some aspects of AI.
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Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult…
Abstract
Purpose
Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.
Design/methodology/approach
Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.
Findings
The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.
Originality/value
This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.
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Amir Khushk, Liu Zhiying, Xu Yi and Xiaolan Zhang
The purpose of this study is to investigate the key characteristics of artificial intelligence (AI) in organizational settings, analyze its capacity to reduce customer service…
Abstract
Purpose
The purpose of this study is to investigate the key characteristics of artificial intelligence (AI) in organizational settings, analyze its capacity to reduce customer service jobs in favor of more advanced roles and analyze its efficacy in candidate screening by emphasizing performance.
Design/methodology/approach
A comprehensive analysis of 40 papers is performed using the PRISMA method based on data from Web of Science, Scopus, Emerald and Google Scholar.
Findings
The findings show optimized human resource management operations such as recruiting and performance monitoring, resulting in increased precision in hiring and decreased employee turnover. Customer service automation redistributes human labor to more intricate positions that need analytical reasoning and empathetic skills.
Practical implications
The study has two key implications. First, AI can streamline customer service, freeing up human workers for more complex tasks. Second, AI may increase candidate screening accuracy and efficiency, improving recruiting outcomes and organizational performance.
Originality/value
The study adds to the current literature by shedding light on the intricate relationships between AI and organizational performance and providing insights into the processes underpinning trust-building in AI technology.
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Rupali Misra Nigam, Sumita Srivastava and Devinder Kumar Banwet
The purpose of this paper is to review the insights provided by behavioral finance studies conducted in the last decade (2006-2015) examining behavioral variables in financial…
Abstract
Purpose
The purpose of this paper is to review the insights provided by behavioral finance studies conducted in the last decade (2006-2015) examining behavioral variables in financial decision making.
Design/methodology/approach
The literature review assesses 623 qualitative and quantitative studies published in various international refereed journals and identifies possible scope of future work.
Findings
The paper identifies stock market anomalies which contradict rational agents of modern portfolio theory at an aggregate level and behavioral mediators, influencing the financial decision making at an investor level. The paper also attempts to classify different dimensions of risk as professed by the investor.
Originality/value
The authors synthesize the contribution made by behavioral finance studies in extending the knowledge of financial market and investor behavior.
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Hakim Sadou, Tarik Hacib, Hulusi Acikgoz, Yann Le-Bihan, Olivier Meyer and Mohamed Rachid Mekideche
The principle of microwave characterization of dielectric materials using open-ended coaxial line probe is to link the dielectric properties of the sample under test to the…
Abstract
Purpose
The principle of microwave characterization of dielectric materials using open-ended coaxial line probe is to link the dielectric properties of the sample under test to the measurements of the probe admittance (Y(f) = G(f)+ jB(f )). The purpose of this paper is to develop an alternative inversion tool able to predict the evolution of the complex permittivity (ε = ε′ – jε″) on a broad band frequency (f from 1 MHz to 1.8 GHz).
Design/methodology/approach
The inverse problem is solved using adaptive network based fuzzy inference system (ANFIS) which needs the creation of a database for its learning. Unfortunately, train ANFIS using f, G and B as inputs has given unsatisfying results. Therefore, an inputs selection procedure is used to select the three optimal inputs from new inputs, created mathematically from original ones, using the Jang method.
Findings
Inversion results of measurements give, after training, in real time the complex permittivity of solid and liquid samples with a very good accuracy which prove the applicability of ANFIS to solve inverse problems in microwave characterization.
Originality/value
The originality of this paper consists on the use of ANFIS with input selection procedure based on the Jang method to solve the inverse problem where the three optimal inputs are selected from 26 new inputs created mathematically from original ones (f, G and B).