Search results

Online shopping cart abandonment presents a major problem for online fashion apparel retailers today. This exploratory research aims to validate scales that measure antecedents of online shopping cart abandonment (OSCA) and examine how these reasons contribute to OSCA behaviour. The findings indicated that the eight different reasons (financial reasons, organisational tool, time pressure, intangibility, privacy issues, aesthetic design, social influences and entertainment factors) that drive OSCA are distinct and account for unique variance in the model, validating the measures. Also, the findings revealed that financial reasons and using the cart as an organisational tool are the top two reasons why consumers abandon their carts. This study provides researchers with a better theoretical understanding of the reasons why consumers abandon their online shopping carts. It validates the various reasons why consumers abandon their shopping carts and provides valuable managerial insights on how online marketers may enhance the translation of online browsing behaviour into actual purchases.

This study aimed to explain how a framework could be developed for (1) the preliminary estimation of project safety level (PSL) in current projects, (2) the estimation of the maximum possible PSL using limited financial resources and (3) the estimation of the minimum financial resources required for reaching a specific PSL.

The data of 95 steel structural building projects were collected via a questionnaire to evaluate the proposed framework for the Iranian construction industry. Based on unofficial local construction statistics and literature reviews, six safety influential factors (SIFs) were selected to which a cost could be assigned. The costs associated with various levels were also determined for each SIF through literature reviews and expert interviews. A multiple linear regression (MLR) model was developed as a predictive model to determine PSL for future projects based on the data of previous projects. Moreover, linear programming (LP) was applied to take modeling constraints and project conditions into account.

The results demonstrated the impacts of all the factors on PSL and the model's potential for the preliminary estimation of PSL using SIFs. The results also indicated that a higher PSL could be achieved by optimizing the allocation of financial resources to each SIF.

This study contributes to the existing body of knowledge by developing a step-by-step framework to identify an optimal safety cost allocation (OSCA) to achieve the maximum possible PSL using a limited safety budget and considering the data of similar projects. The main objective was to promote project safety, decrease construction site injuries and fatalities and help local construction industries exploit potential financial advantages.

Optical sensor companies in the UK are getting together to organise their own research and development.

Today, big data (BD) is considered as a crucial investment for firms to stay competitive. However, the human resource (HR) function within small- and medium-sized enterprises (SMEs) has been slow to adopt this innovation. Drawing on the organizational learning theory (OLT), this study aims to propose that BD can improve HR functions, especially of SMEs, thereby yielding them a competitive edge.

This study analyzed unstructured data from 41 journal papers, based on which, a conceptual framework was developed. Further, this framework was validated with responses collected from 148 SMEs in India.

Bibliometric analysis and results of partial least squares techniques revealed that better BD quality is needed to improve HR practices, human resource service quality (HRSQ) and innovation competency of SMEs.

This paper contributes to the extant literature by considering strategic management theories such as resource-based view and OLT to evaluate BDA’s effect on organizational functional practices such as HR and HRSQ.

In Indian SMEs, BD quality has a substantial effect on BD HR practices and HRSQ. However, these factors influence can constructively impact SMEs, if SMEs are open to organizational change, whereby they need to develop technical skills and competencies of the HR professionals.

Though BD research works have shown exponential growth in recent times, scholarly empirical research investigating BD’s impact upon human resource management (HRM) is scarce. The present study appraises extant literature on BD in HRM.

Describes how the Office of Strategic Crime Assessments (OSCA) and the Victoria Police Computer Crime Investigation Squad conducted the 1997 Computer Crime and Security Survey in order to establish some reliable base‐line information regarding the extent of computer‐related crime in Australia today. A representative sample of over 300 Australian companies was surveyed. Of the respondents, 37 per cent had experienced some form of intrusion or other unauthorised use of computer systems in the last 12 months. Nearly 90 per cent of those that had experienced computer‐related incidents had been subjected to attacks from sources internal to their own organisation. Over 60 per cent were subjected to intrusions from external sources (meaning that a significant number of companies had been subjected to attacks from both employees and outsiders). States that the results of this study empirically support many elements of the anecdotal evidence and highlights a number of issues for Australian law enforcement.

With a focus on the evolution of human resource management (HRM) quantification over 2000–2020, this study addresses the following questions: (1) What are the data sources used to quantify HRM? (2) What are the methods used to quantify HRM? (3) What are the objectives of HRM quantification? (4) What are the representations of quantification in HRM?

This study is based on an integrative synthesis of 94 published peer-reviewed empirical and non-empirical articles on the use of quantification in HRM. It uses the theoretical framework of the sociology of quantification.

The analysis shows that there have been several changes in HRM quantification over 2000–2020 in terms of data sources, methods and objectives. Meanwhile, representations of quantification have evolved relatively little; it is still considered as a tool, and this ignores the possible conflicts and subjectivity associated with the use of quantification.

This literature review addresses the use of quantification in HRM in general and is thus larger in scope than previous reviews. Notably, it brings forth new insights on possible differences between the main uses of quantification in HRM, as well as on artificial intelligence and algorithms in HRM.

Enabled by increased (“big”) data stocks and advanced (“machine learning”) analyses, the concept of human resource analytics (HRA) is expected to systematically improve decisions in human resource management (HRM). Since so far empirical evidence on this is, however, lacking, the authors' study examines which combinations of data and analyses are employed and which combinations deliver on the promise of improved decision quality.

Theoretically, the paper employs a neo-configurational approach for founding and conceptualizing HRA. Methodically, based on a sample of German organizations, two varieties (crisp set and multi-value) of qualitative comparative analysis (QCA) are employed to identify combinations of data and analyses sufficient and necessary for HRA success.

The authors' study identifies existing configurations of data and analyses in HRM and uncovers which of these configurations cause improved decision quality. By evidencing that and which combinations of data and analyses conjuncturally cause decision quality, the authors' study provides a first confirmation of HRA success.

Major limitations refer to the cross-sectional and national sample and the usage of subjective measures. Major implications are the suitability of neo-configurational approaches for future research on HRA, while deeper conceptualizing and researching both the characteristics and outcomes of HRA constitutes a core future task.

The authors' paper employs an innovative theoretical-methodical approach to explain and analyze conditions that conjuncturally cause decision quality therewith offering much needed empirical evidence on HRA success.

The purpose of this paper is to explore how positive organizational behaviors, specifically team proactivity, can be captured through digital data and what determines content validity of these data. The aim is to enable scientifically rigorous HR analytics projects for measuring and managing organizational behavior.

Results are derived from interview data (N = 24) with team members, HR professionals and consultants of HR software.

Based on inductive qualitative content analysis, the authors clustered six data types generated/recorded by 13 different technological applications that were proposed to be informative of team proactivity. Four determinants of content validity were derived.

The overview of technological applications and resulting data types can stimulate diverse HR analytics projects, which can contribute to organizational performance. The authors suggest ways to control for the threats to content validity in the design of HR analytics or research projects.

HR analytics projects in the application field of managing organizational behavior are rare. This paper provides starting points for choosing data to measure team proactivity as one form of organizational behavior and guidelines for ensuring their validity.

Human resource management (HRM) processes are increasingly artificial intelligence (AI)-driven, and HRM supports the general digital transformation of companies' viable competitiveness. This paper points out possible positive and negative effects on HRM, workplaces and workers’ organizations along the HR processes and its potential for competitive advantage in regard to managerial decisions on AI implementation regarding augmentation and automation of work.

A systematic literature review that includes 62 international journals across different disciplines and contains top-tier academic and German practitioner journals was conducted. The literature analysis applies the resource-based view (RBV) as a lens through which to explore AI-driven HRM as a potential source of organizational capabilities.

The analysis shows four ambiguities for AI-driven HRM that might support sustainable company development or might prevent AI application: job design, transparency, performance and data ambiguity. A limited scholarly discussion with very few empirical studies can be stated. To date, research has mainly focused on HRM in general, recruiting and HR analytics in particular.

The four ambiguities' context-specific potential for capability building in firms is indicated, and research avenues are developed.

This paper critically explores AI-driven HRM and structures context-specific potential for capability building along four ambiguities that must be addressed by HRM to strategically contribute to an organization's competitive advantage.

Purpose: In this chapter we examine the emotional experience and identity of sustainability change agents and advance understanding of their emotion management strategies. We explore how sustainability change agents experience, manage, and respond to the negative emotions that arise in the course of their jobs. Study Design: We took a mixed-method and multimodal approach to answer our research questions. Using a narrative approach, we collected data using in-depth narrative interviews and supplemented this with quantitative measurement of participants' heart rate and sweat response during the interviews. Findings: Our results confirm that sustainability change agency is an emotionally laden profession. Furthermore, we found that sustainability change agents use three different coping mechanisms including emotion-focused coping (EFC) (“rational avoiders”), problem-focused coping (PFC) (“committed go-getters”), and meaning-focused coping (MFC) (“green philosophers”). Originality: Our research shows that sustainability change agents experienced strong negative emotions in relation to their jobs and they employed one of the three coping styles: EFC, PFC, or MFC. We found that MFC was an isolated cognitive appraisal style, rather than a form of EFC. These findings provide a starting point for further work to help sustainability change agents avoid potential burnout and continue to contribute to the future health of the planet while at the same time maintain their personal well-being.