Kenneth Cafferkey, Keith Townsend, Safa Riaz, Ester Ellen Trees Bolt and Md Shamirul Islam
This study aims to investigate the relationships between various frontline management (FLM) styles, human resource management system (HRM) system strength and employees' helping…
Abstract
Purpose
This study aims to investigate the relationships between various frontline management (FLM) styles, human resource management system (HRM) system strength and employees' helping behaviours as a form of organisational citizenship behaviours (OCBs). The research also examines the moderating role of workgroup loyalty in the association between HRM system strength and employees' helping behaviours.
Design/methodology/approach
The research uses survey data collected from 315 government workers in Malaysia. Structural equation modelling (SEM) was employed to test the hypothesised relationships.
Findings
Two FLM styles, “policy enactor” and “employee coach,” positively predict employees' helping behaviour. However, the “organisational leader” FLM style did not significantly lead to employees' helping behaviour. HRM system strength significantly mediates the relationship between the three FLM styles and employee helping behaviours. Finally, workgroup loyalty significantly moderates the relationship between HRM system strength and employees’ helping behaviours as OCB.
Practical implications
With a wealth of literature demonstrating the importance of FLMs in the implementation of HRM and a growing body of literature demonstrating the robust nature of the “system strength” argument, human resource (HR) practitioners are increasingly able to focus their attention on the way the system and FLMs contribute to employee outcomes and organisational performance. Our results indicate that HRM system strength does indeed enhance the impact of FLM styles on employee helping behaviours.
Originality/value
The originality of this paper is that it acknowledges and empirically examines the heterogenous nature of FLM styles, through signalling theory in enacting HRM policies and links the growing FLM literature to the HRM system strength research. These concepts have also been tested for the first time in a Malaysian context.
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Arjun Hans, Farah S. Choudhary and Tapas Sudan
The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these…
Abstract
Purpose
The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.
Design/methodology/approach
The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.
Findings
The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.
Research limitations/implications
The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.
Originality/value
Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.
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Eric Ohene, Gabriel Nani, Maxwell Fordjour Antwi-Afari, Amos Darko, Lydia Agyapomaa Addai and Edem Horvey
Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted…
Abstract
Purpose
Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.
Design/methodology/approach
This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.
Findings
The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.
Originality/value
The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.
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Sara Munir, Mazhar Farid Chishti and Rizwana Bashir
The cognitive biases exhibited by investors could hinder their capacity for logical reasoning and impact their perception and reaction to information when making financial…
Abstract
Purpose
The cognitive biases exhibited by investors could hinder their capacity for logical reasoning and impact their perception and reaction to information when making financial choices. So, this study was done to identify the behavioral biases that hinder investors' sound decision-making at the Pakistan Stock Exchange (PSX).
Design/methodology/approach
A cross-sectional study was undertaken employing a causal research design approach. Questionnaires were administered to individual investors of the PSX as the data collection methodology. The data were subsequently analyzed through the utilization of the Smart PLS Structural Equation Modeling (SEM) technique.
Findings
The results suggest that information factors and cognitive biases, namely home bias, geographical bias, investor sentiment, salience, and over/under reaction have a positive association with the investors' choices at PSX.
Research limitations/implications
The study’s emphasis is on the impact of behavioral biases on individual investors only, even though such biases also influence the investment decisions of institutional investors.
Practical implications
The study holds implications for scholars engaged in the field of behavioral finance as well as professionals involved in the stock market, particularly those interacting with individual investors and personal finance. Additionally, the current study will take into account investors, financial advisors, practitioners, policymakers, investment experts, stakeholders or target groups, etc. to support various groups in their professional activity and to help them overcome such biases that influence their sound decision-making power.
Originality/value
The innovative aspect of this research is its ability to advance the understanding of the conceptual underpinnings and social structure of behavioral biases by critically analyzing the body of prior research and adding value to the existing body of literature on behavioral finance in Pakistan by investigating the combined impact of never-studied variables, i.e. geographical bias and information variables, understudied behavioral variables, i.e. home bias and salience and studied variables, i.e. investor sentiment and over/under reaction on individual investor investment decisions at PSX.