Business Process Management Journal最新文献

Purpose

This research paper aims to integrate the employee-related factors that empirical literature considers antecedents of performance (skills, work motivation, personal characteristics) into a multiple linear regression model, and to test such a model in order to measure the level of each individual factor on the performance.

Design/methodology/approach

Quantitative, multisource research approach. After testing the validity of the model with a Confirmatory Factor Analysis, this research applies the multiple linear regression model Work performance = a(Skills) + b(Work Motivation) + c(Personal Characteristics) + e(constant) to two different samples of workers: chemical technicians (N = 63) and salespeople (N = 61).

Findings

This study confirms the factorial structure of the antecedents of work performance, showing that skills, motivation, and personal characteristics are three general employee-related factors underlying work performance. The statistical analysis highlights a variance in performance between 40 and 65% explained by employee-related factors, hence leaving 35–60% as due to factors outside the model (firm/environment-related and/or job-related factors, or other skills and personal characteristics not considered in the model). The study also highlights that employee-related factors sometimes affect performance differently than job designers' expectations, and sometimes even negatively.

Research limitations/implications

The equation was tested on two case studies, so further explorations are needed. Furthermore, the approach adopted is inductive thus describing performance as it is, not as it should be. Therefore, it explains the best actual performance of workers, not the ideal performance.

Practical implications

The equation tested here represents a simple and valid tool to guide many Human Resource Management practices, such as; selection, training, development, and career orientation.

Social implications

Findings provide a valid indication for designing and managing human resource management systems more even-handedly, from an organizational and employee point of view. In doing so, it drives organizations towards a better Person/Job fit.

Originality/value

The study represents one of the first attempts to take into consideration multiple factors simultaneously in explaining work performance.

Purpose

The objective of this work is to demonstrate how the use of a business process management (BPM) methodology reinforced with the use of lean manufacturing (LM) tools and practices enhances information and documentation flows.

Design/methodology/approach

For this purpose, a case study on a large wind blade manufacturing company is described, in which BPM and LM were combined to improve information and documentation flows associated to the process of quality inspections and quality controls.

Findings

The joint use of BPM and LM strongly contributed to the improvement of information and documentation flows. The BPM lifecycle can be used to guide the entire improvement process, while LM tools can be used to act at specific points with an emphasis in the process analysis and implementation phases. Thus, LM complements a BPM approach leading to significant process improvements.

Practical implications

The results show that LM can be used to support some phases of the BPM lifecycle. Furthermore, LM can contribute to identify lean waste in information and documentation flows associated to quality management processes and help in the selection of methods and tools to support process improvements.

Originality/value

This study is one of the first reporting the use of LM tools and practices as complementary to the BPM methodology to support the improvement of information and documentation flows associated to quality management in a large manufacturing company. This research enriches the literature by presenting empirical evidence that these two continuous improvement approaches are not incompatible in their objectives and visions and can complement each other.

Purpose

The purpose of this paper is to investigate the organizational readiness perspective of adopting artificial intelligence and big data analytics in the textile and garment industry in Bangladesh along with identifying the associated factors.

Design/methodology/approach

The research uses a qualitative method using semi-structured interviews with representatives of business organizations and stakeholders of Bangladesh’s textile and garment industry.

Findings

The research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics. This is due to moderate knowledge- and leadership-readiness along with low human-, finance- and engagement-readiness of most of the business organizations. The readiness aspects interplay with each other and need to be improved holistically.

Practical implications

Considering the significant global and national importance of Bangladesh’s textile and garment industry, gaining insights into the industry’s current state of readiness for adopting artificial intelligence and big data analytics would offer valuable assistance to both national and global economies and may enhance economic outcomes.

Originality/value

Since no exploratory study was conducted to understand the organizational readiness aspects of adopting artificial intelligence and big data analytics of the globally significant textile and garment industry in Bangladesh, the paper analyzes five key aspects of such readiness and offers a basis for conducting similar studies in other emerging economies.

Purpose

In the context of open innovation, more and more enterprises are leveraging innovation networks to drive disruptive innovation performance, but there is no consensus on the relationship between network embeddedness and enterprise disruptive innovation performance. This paper aims to systematically explore the relationship between them.

Design/methodology/approach

This paper constructs a multi-level network embeddedness model and uses 58 independent studies as samples to explore the relationship between multi-level network embeddedness and enterprise disruptive innovation performance by meta-analysis.

Findings

First, network embeddedness at the enterprise and regional levels will promote the improvement of disruptive innovation performance. Although industrial relationship embeddedness will promote the improvement of disruptive innovation performance, its structural embeddedness will bring negative effects. Second, in terms of mediating effect, policy-oriented support will promote the relationship between network embeddedness and disruptive innovation performance at the enterprise and industry levels. Compared with large enterprises, small- and medium-sized enterprises will have more advantages in the performance of multi-level network embedding and disruptive innovation performance. Under the subjective performance measurement method, the promotion effect of multi-level network embedding is more prominent.

Research limitations/implications

This study enriches the theoretical research of network embeddedness and disruptive innovation and provides management enlightenment for the network embeddedness strategy of enterprise disruptive innovation. Limited by data samples and article length, future research can further expand literature samples to test the stability of variable relationships and test the moderating effects of more internal and external factors.

Originality/value

First, it constructs a theoretical analysis model of “point-line-surface” multi-level network embedding and disruptive innovation performance of enterprises and expands the theoretical analysis framework of network embedding and disruptive innovation performance. The second is to explore the influence mechanism of multi-level network embeddedness and enterprise disruptive innovation performance. Third, it deepens the theoretical understanding of the moderating variables of the impact of network embeddedness and enterprise disruptive innovation performance.

Purpose

The patient flow performance achievable by care pathways is constrained by competing flow and resource efficiency, which can negatively impact improvements. This paper probes the divergence between resource and flow efficiency and how care pathways can lead to improved patient flow. By framing the problem through the lens of paradox theory, a set of design principles is proposed to assist decision-makers in care pathway implementation. Implications are derived for research and practice.

Design/methodology/approach

The authors used conceptual research to develop design principles for care pathways based on a systematic review of relevant care pathway research. The initial search contained 515 unique articles, resulting in a final sample of 56 studies.

Findings

When applying care pathways, patient flow may be negatively affected in relation to the dimensions of bottlenecks, non-value-adding activities, and variability. However, the findings also indicate methods that can be applied to manage organizational paradoxes, which can contribute to more efficient patient flow along each of the three dimensions.

Research limitations/implications

The study is limited to care pathways and therefore could have missed relevant studies in similar fields, such as care coordination.

Practical implications

Health care managers, politicians, and IT developers can apply the proposed design principles when developing, implementing, and improving care pathways and supporting technologies.

Originality/value

While existing research has studied care pathways from a medical perspective, this is the first paper to the author’s knowledge that addresses care pathways directly by considering paradox theory and in light of the operations management literature.

Purpose

This study investigates the factors that lead to the adoption of blockchain technology through payment transactions and how this not only affects real estate (RE) and blockchain transparency but also RE performance.

Design/methodology/approach

Data gathered across RE firms in the United Arab Emirates (UAE) were employed to test the model. The measurement model and structural equation modeling (SEM) were used to test the items and the hypotheses illustrated in the proposed model.

Findings

Perceived financial benefits, competitive pressure and top manager support were demonstrated to successfully influence blockchain adoption (BA). Despite blockchain’s early stages of development, its impact on RE operations cannot be ignored and should be more objectively examined in order to gain a better understanding of it. UAE blockchain-based companies could be seen as having a competitive advantage that maximizes resource consumption.

Originality/value

This study introduces the positive influence of blockchain technology on RE payment transactions and may advance information on how blockchain technology has the potential to change the RE sector. The paper finds its significance in exploring how RE payment systems must change to remain competitive in the market amid emerging digitalization trends.

Purpose

This study applies organizational information processing theory (OIPT) to investigate managing production process variability in uncertain environments using information technology (IT) capabilities.

Design/methodology/approach

We conduct an empirical analysis using the three-stage least squares (3SLS) technique on 1,612 manufacturing firms over ten years.

Findings

The findings show that IT capability plays a dual role: it reduces the positive impact of environmental uncertainty on production process variability and mitigates the negative relationship between production process variability and operational performance.

Practical implications

Our findings suggest that managers should focus on reducing production process variability by strengthening their firms' IT capabilities. This is particularly crucial in volatile environments where external uncertainties can significantly impact operational processes.

Originality/value

Variability in the production process is a significant source of inefficiency and disruption within business processes. Using OIPT, our study contributes to the field by empirically analyzing the role of IT capabilities in reducing production process variability under environmental uncertainty.

Purpose

How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

Design/methodology/approach

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Findings

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Originality/value

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.