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The purpose of this paper is to explore the impact of financial regulation policy uncertainty (FRPU) on bank profit and risk.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The literature review assesses 623 qualitative and quantitative studies published in various international refereed journals and identifies possible scope of future work.

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.

The authors synthesize the contribution made by behavioral finance studies in extending the knowledge of financial market and investor behavior.

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).

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.

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.

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).