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The purpose of this paper is to contribute to the extant literature about the co-evolvement of Business Process Management (BPM) and the Internet of Things (IoT) by proposing the IoT-enabled Context-aware BPM (IoT-CaBPM) framework to bridge from the IoT infrastructure to context-aware business processes.

Motivated by the “Three Waves” of BPM research, IoT-enabled context-awareness is, therefore, expected to be achieved for enhancing the business process design, which pilots a new wave of BPR (Business Process Redesign/Reengineering) to enable the business process coevolve with IoT and analytics. This paper reports an illustrative case study of BPR in a Chinese bulk port, one of the hub seaports that widely adopted IoT technologies over the last few years.

The IoT implementation and data analytics has increased the efficiency and improve the monitoring effectively. The proposed IoT-CaBPM framework availably helps to identify and match nodes of IoT devices, business decisions and analytic models in order to redesign a business process towards context-aware variability. As IoT is rapidly becoming the new dominant IT paradigm is moving towards mature implementation in various industries, the corresponding BPR must be planned and executed strategically for achieving better benefits.

Despite some research extend BPM standard by integrating IoT devices as a sort of resources or report generically that the ports operations are affected by IoT, there is still a lack of layers from the IoT infrastructure to context-aware business processes. An industrial BPR case with business models in detail is also a lack for presenting the specific implications and effectiveness of the adoption of such technologies. This paper fills in this gap.

This paper aims that digital transformation (DT) is crucial for companies to stay competitive. While research on DT has quickly gained great popularity, the intersection of trade associations (TAs) and their role in the DT of their members is not yet researched.

In this paper, the authors conducted 20 interviews with Belgian TAs to investigate the role of a TAs in the DT of its members, and how they drive the DT of its members. In addition, the authors investigate the core tasks of TAs, the need of the different industries to digitalize, and the digital projects the different industries are working on.

The findings indicate that TAs can be in a prime position to steer the DT of their members, especially for industries comprised of smaller players. Their roles can range from informing roles to true leaders of DT by creating novel products, such as online platforms and driving the entire sector forwards.

These findings call for more research into TAs and how their role can be optimized for steering DT of their members.

This is the first study to extensively study the role of TAs on the DT of their members.

Digital transformation (DT) projects are complex and often unsuccessful. While researchers have suggested many guidelines and best practices on how to successfully roll out DT projects and how they are spread among a large number of scientific papers. The aim of this paper is to synthesize these guidelines into clear overviews.

A systematic literature review was conducted on both Scopus and Web of Science to search for papers suggesting DT guidelines or best practices. In total, 150 papers dealing with DT and guidelines were fully analyzed.

Eight main DT guidelines were found and each one was expanded with several best practices on how to implement these. The results are eight tables giving an overview of the commonly agreed-upon best practices for each DT guideline.

These overviews are useful for both researchers and practitioners, to guide future work and to be inspired respectively. This paper calls for more research on how these guidelines are followed in practice, how these differ per industry and what their impact is on the overall success of DT projects.

The synthesis of DT guidelines organized into an accessible format has not yet been conducted before, and can serve as a seminal pinpoint for future research.

Probability of Default (PD) is a crucial credit risk parameter. International accords have motivated banks and credit institutions to adopt objective systems of evaluating and monitoring the PD. This study examines retail unsecured loans of a major Greek bank during the period of the financial crisis. It focusses on the stochastic behaviour of the financial states of the loans. It is tested whether a first-order Markov chain (MC) model describes sufficiently the transitions from one state to another. Moreover, Poisson regression models are estimated in order to calculate the limiting transition matrix, the limiting state probabilities and the PD. It is proved that the MC of the financial states of loans is non-homogeneous suggesting that the transition probabilities from one financial state to another are not constant across time. From the Poisson regression models, the transition probability matrix is estimated from one state to another in alternative time periods. From the limiting transition matrix, it is shown that if a loan is delayed then it is very likely to move towards the next worst case. The findings of this research could be useful for bank management.

Process mining (PM) specialises in extracting insights from event logs to facilitate the improvement of an organisation's business processes. Industry trends show the proliferation and continued growth of PM techniques. To address the minimal attention given to developing empirically supported frameworks to assess the nature of impact in the PM domain, this study proposes a framework that identifies the key categories of PM impacts and their interrelationships.

The qualitatively derived framework is built, re-specified and validated from a diverse collection of 62 PM case reports. With multiple rounds of coding supported by coder corroborations, inductively extracted concepts relating to impact from a first set of 12 case reports were grouped into themes and sub-themes to derive an a-priori framework by adopting the balanced scorecard as a theoretical lens. Concepts from the remaining 50 case reports were deductively grouped to re-specify and validate the proposed PM impacts framework. Further analysis identified interrelationships between impacts, which extends our understanding of the identified PM impacts.

The proposed framework captures PM impacts in four main categories: (a) impact on the process, (b) customer impact, (c) financial impact, and (d) impact on innovation and learning. The authors extended this analysis to identify the interrelationships between these categories, which vividly demonstrates how impact on the process mediates the attainment of the other three impact types.

The need for a deeper understanding of PM impacts within the context of contemporary PM practice is addressed by this work. The PM impacts framework provides a classification of PM impacts into four categories with 19 subcategories. It also identifies direct, moderating and mediating relationships between categories and subcategories whilst highlighting the role of impact on the process as a precursor to the other types of PM impact.

The purpose of this paper is to review empirical studies on process mining in order to understand its use by organizations. The paper further aims to outline future research opportunities.

The authors propose a classification model that combines core conceptual elements of process mining with prior models from technology classification from the enterprise resource planning and business intelligence field. The model incorporates an organizational usage, a system-orientation and service nature, adding a focus on physical services. The application is based on a systematic literature review of 144 research papers.

The results show that, thus far, the literature has been chiefly concerned with realization of single business process management systems in single organizations. The authors conclude that cross-system or cross-organizational process mining is underrepresented in the ISR, as is the analysis of physical services.

Process mining researchers have paid little attention to utilizing complex use cases and mining mixed physical-digital services. Practitioners should work closely with academics to overcome these knowledge gaps. Only then will process mining be on the cusp of becoming a technology that allows new insights into customer processes by supplying business operations with valuable and detailed information.

Despite the scientific interest in process mining, particularly scant attention has been given by researchers to investigating its use in relatively complex scenarios, e.g., cross-system and cross-organizational process mining. Furthermore, coverage on the use of process mining from a service perspective is limited, which fails to reflect the marketing and business context of most contemporary organizations, wherein the importance of such scenarios is widely acknowledged. The small number of studies encountered may be due to a lack of knowledge about the potential of such scenarios as well as successful examples, a situation the authors seek to remedy with this study.

The purpose of this paper is to develop an insight into the skill sets that forensic accounting practitioners need to possess to succeed in the practice of forensic accounting.

The present paper is based on a literature review.

Forensic accounting education is multi-disciplinary. It encompasses auditing, accounting, statistics, information technology (IT), legal rules and human skills. It is similar to auditing, yet different. Hands-on statistical tools act like an additional equipment for quick delivery of the output when data are large. Proficiency in using IT tools is a must to detect cybercrimes. Human skills are gaining importance because of social engineering attacks. Forensic accountants must be acquainted and updated with the relevant laws. Various investigative skills and knowledge are also essential in forensic accounting.

Forensic accounting education can be developed as a separate discipline for proper regulation of forensic accounting profession. In that case, the need for development of separate forensic accounting standards may arise. This issue needs to be dwelt upon by the academia and professional bodies.

The paper will enable the universities/institutes to design the appropriate curricula, assigning due consideration to the required knowledge and skill sets in forensic accounting education.

This paper aims to review the application of business process management (BPM) in the port sector. Its objective is to understand whether BPM principles are applied in the port sector, the role of the procedural factor in port performance evaluation and whether electronic data interchange systems have been used for process management purposes.

The objective of this research is to conduct a critical review of existing academic literature in the domain of BPM and its application in the ports sector. This paper assessed more than a hundred recent publications, from key journals in the domains of port economics, BPM and information technology. The two principle platforms used are the online databases of the World Bank Group and the University of Antwerp.

Academic literature reviewed reveals a partial application of BPM in the port and maritime sector. BPM related research is conducted via the utilization of modeling algorithms or optimization and simulation tools. There exists evidence that electronic data interchange (EDI) data extracted from EDI platforms can be used to model inter-organizational business processes in several industries. Yet, to the best of the author’s knowledge, no research investigates Port Community System (PCS) or single window (SW) data utilization for BPM purposes, although PCS and SW benefits are well documented. Port performance is largely assessed based on the production theory, and limited number of studies use elements of procedural efficiency as variables for their analysis.

The holistic application of BPM has been researched in numerous industries but in the port sector. This paper constitutes the first section of an original research study to define key components, assumptions and constraints for developing a comprehensive BPM framework in the port sector.

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

The literature on HR metrics does not address the political issues that surround the definition and interpretation of these metrics. However, the need for agreement about HR metrics between different stakeholders is underlined by the framework of the sociology of quantification. This research aims to complement the literature on HR metrics by studying the process of defining and interpreting appropriate HR metrics, focusing on the example of the gender pay gap.

A case study with interviews and participant observation is conducted on a French company.

The results show that HR metrics are essential to prove the existence of inequalities. However, there are disagreements between management, unions and gender equality referents concerning appropriate HR metrics and their use.

Contrary to the literature on the subject, which remains predominantly positivist and normative, this article sheds light on the political issues surrounding the definition and use of HR metrics and shows the importance of involving a range of actors (managers, unions and employees) in the entire process.