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Testing business processes is crucial to assess the compliance of business process models with requirements. Automating this task optimizes testing efforts and reduces human error while also providing improvement insights for the business process modeling activity. The primary purposes of this paper are to conduct a literature review of Business Process Model and Notation (BPMN) testing and formal verification and to propose the Business Process Evaluation and Research Framework for Enhancement and Continuous Testing (bPERFECT) framework, which aims to guide business process testing (BPT) research and implementation. Secondary objectives include (1) eliciting the existing types of testing, (2) evaluating their impact on efficiency and (3) assessing the formal verification techniques that complement testing.

The methodology used is based on Kitchenham's (2004) original procedures for conducting systematic literature reviews.

Results of this study indicate that three distinct business process model testing types can be found in the literature: black/gray-box, regression and integration. Testing and verification approaches differ in aspects such as awareness of test data, coverage criteria and auxiliary representations used. However, most solutions pose notable hindrances, such as BPMN element limitations, that lead to limited practicality.

The databases selected in the review protocol may have excluded relevant studies on this topic. More databases and gray literature could also be considered for inclusion in this review.

Three main originality aspects are identified in this study as follows: (1) the classification of process model testing types, (2) the future trends foreseen for BPMN model testing and verification and (3) the bPERFECT framework for testing business processes.

Over 8 per cent of the Dutch nursing home population is bedfast, and this number is slowly increasing. The quality of life (QoL) of this population is lower than that of residents who are still mobile. Little research has been conducted on how to improve the QoL of this bedfast population, particularly through making technological adjustments to the bed and the direct surroundings. The purpose of this paper is to gain insight into the QoL of bedfast residents and how to improve this through technology.

A mixed-method multi-case study with thematic analysis was conducted in two nursing homes with seven participants based on semi-structured interviews and Short Form-12 questionnaire.

The major causes of the experienced low QoL were the limited opportunities for engaging in social contacts with others, and coping with the dependency on other people and having limited control. Participants suggested improvements of QoL through the application of modern communication technologies to engage in social contacts and to control the bed itself and environment around the bed.

The results may help improve the design of the bed and the direct environment in order to improve the QoL of bedfast nursing home residents.

The QoL of bedfast nursing home residents has not been studied before in relation to the bed itself and technological solutions that may help improve the QoL and level of control.

Medical training is a foundation on which better health care quality has been built. Freshly graduated doctors have required a good knowledge of practical competencies, which demands the importance of medical training activities. As of this, we propose a methodology to discover a process model for identifying the sequence of medical training activities that had implemented in the installation of a Central Venous Catheter (CVC) with the ultrasound technique. A dataset with twenty medical video recordings were composed with events in the CVC installation. To develop the process model, the adoption of process mining techniques of infrequent Inductive Miner (iIM) with a noise threshold value of 0.3 had done. A combination of parallel and sequential events of the process model was developed. Besides, process conformance was validated with replay fitness value about 61.1%, and it provided evidence that four activities were not correctly fit in the process model. The present study can assist upcoming doctors involved in CVCs surgery by providing continuous training and feedback on better patient care.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

Forests support human livelihoods and mitigate against climate change, yet they are at a risk of irreversible loss due to high degradation rates. The success of forest conservation mechanisms depends on involvement and support by forest dependent communities. In this paper, the authors assess forest dependent household's willingness to pay (WTP) labour or cash for a conservation programme seeking to restore degraded forestland in Mount Elgon Forest, Kenya.

Data were collected from 919 households in Mt Elgon forest reserve, Kenya. A double bounded contingent valuation approach was used to examine households' WTP and an ordered probit model to estimate the determinants of WTP.

The findings of the study show a higher WTP for conservation through labour days (12 days/month, equivalent to 1800 KES/month) compared to cash (KES 450/month). Forest dependence has a significant influence on households' willingness to support conservation activities. A higher WTP was observed amongst households with higher vulnerability (high shocks value, low asset value and those in the poorest wealth categories) implying that they are more willing to contribute for forest conservation.

While emerging literature on WTP for forest conservation is growing, few studies have paid attention on the influence of forest dependence on WTP for forest conservation. There are limited studies on use of in-kind contribution as a payment vehicle for WTP. The study's findings show a high WTP in form of labour suggesting the importance of embracing in-kind contribution as a mechanism of supporting forest conservation in contexts of developing countries.

There is growing interest in the economic impact of workplace physical activity interventions, but the evidence is still lacking — especially in Europe. Although, some evidence on the return on investment (ROI) is found in literature, the included studies may not be applicable to the Europe situation. Therefore, the objective of this study was to review current evidence on the economic impact of workplace physical activity interventions in European countries.

A systematic review on the economic impact of worksite health promotion programs aiming at increasing physical activity was conducted. Five electronic databases (MEDLINE (Ovid), MEDLINE (PubMed), EMBASE, NHS-EED and Emerald Insights) were searched for relevant studies published between 2000 and 2020.

A total of 953 abstracts were screened, and 28 were reviewed, 11 of which met all inclusion criteria. The studies varied substantially in sample size, intervention type, duration and frequency of follow-up measurements, valuation methods and assessed economic outcomes. There is inconclusive evidence for decreasing absenteeism, positive net benefit (NB) and positive ROI. No evidence was found to indicate an effect on self-assessed productivity or job satisfaction.

This study is the first try to take the different working conditions from Europe into consideration. The authors found that working conditions could have some impact on the valuation of absenteeism costs and thereof on the ROI. Further, this study provides insight into how to deploy effective and efficient workplace physical activity interventions, based on a standardized and validated methodology and program scope.

This study aims to explore the role of rebel group legitimacy and ideology in durable peace (DP) following peace agreements to end civil wars. It builds on earlier research showing that justice and civil society involvement are critical in achieving DP. This study adds the impacts of rebel group activities and support on DP. Activities include service delivery and mobilization. Support is gauged with outcomes of presidential and parliamentary elections held following peace agreements.

Five data sets were used to measure the key variables: DP, inclusive commissions (IC), legitimacy symmetry (electoral outcomes), service delivery and ideological mobilization. A measure of rebel group integration in the political system was also constructed. Impacts of the integration, legitimacy and ideology variables were assessed with a hierarchical regression model (HRM). This study begins with a base model drawn from earlier research showing the key predictors were procedural justice (PJ) and IC. The authors ask about the extent to which the rebel group variables contribute additional variance to the prediction of DP.

The main contributors to the prediction of DP were PJ, IC and integration in the political system. None of the legitimacy or mobilization variables added significant variance to the prediction. Only one of the mobilization variables, forced recruitment, was significant. The decision to integrate into the political system following the agreement did not mediate the relationship between PJ in the negotiation process and DP. Results of a factor analysis showed that DP, PJ, IC and integration formed a cluster with strong loadings on the first factor.

The negative results for the legitimacy and mobilization variables may not be the last word on rebel group influences. Lack of support for the key hypotheses spurs attempts to discover other sources that contribute to the survival of rebel group actors in the political system and, in turn, to DP.

The issues raised by this study contribute to debates about ways to attain peaceful relations among competing groups following a civil war. It appears that attention to factors inside and around the negotiation process (PJ, ICs and conversion) may be more important than rebel group activities outside of these processes. The results call attention, in particular, to the important role played by political integration. From a policy perspective, it would be useful to develop levers for encouraging rebel groups to emerge as political actors in the post-agreement environment.

Developing measures of the symmetry of rebel group legitimacy and integration in the context of a comparative case study are the primary original contributions of this study. Furthermore, the mode of analysis (HRM) is novel in this literature. This approach builds on and extends the earlier research on factors influencing DP.

The purpose of this study is to examine the role of videoconferencing technologies for mediating and transforming emotional experiences in virtual context.

Drawing on empirical data of video conferencing experiences, this study identifies different constitutive relations with technology through which actors cope with actual or potential anxieties in virtual meetings. It draws on the phenomenological-existential tradition (Sartre and Merleau-Ponty) and on an interpretive phenomenological analysis (IPA) to conceptualize and illustrate the role of affective affordances in virtual settings.

The study identifies four different body–technology–other relations that provide different action possibilities, both disclosing and concealing, for navigating emotional experiences in virtual encounters of mutual gazing. These findings offer insights into the anatomy of virtual emotions and provide explanations on the nature of Zoom fatigue (interactive exhaustion) and heightened feelings of self-consciousness resulting from video conferencing interactions.

This paper builds on and extends current scholarship on technological affordances, as well as emotions, to suggest that technologies also afford different tactics for navigating emotional experiences. Thus, this paper proposes the notion of affective affordance that can expand current information system (IS) and organization studies (OS) scholarship in important ways. The focus is on videoconference technologies and meetings that have received little research attention and even less so from a perspective on emotions. Importantly, the paper offers nuanced insights that can advance current research discourse on the relationships between technology, human body and emotions.

Secondary customers often experience secondary vulnerabilities that manifest in family-centred transformative services as other- and self-related customer needs. Yet, a relational perspective on primary and secondary customers’ needs is lacking. The study analyses secondary customers’ needs and their relationship to primary customers’ needs to enhance well-being in customer entities. The service inclusion lens is used to understand customers’ experiences of vulnerability.

The study uses an exploratory approach. The data consists of ethnographic observations and interviews of elderly residents (primary customers), their family members (secondary customers) and nurses in two nursing homes.

Primary and secondary customers’ needs are interrelated (or unrelated) in four ways: they are separate, congruent, intertwined or discrepant. The vulnerability experiences fluctuate in intensity and over time, individually reflecting on these need dimensions.

The study contributes to service research concerning customers’ experiences of vulnerability, secondary customers and their inclusion in services. Primary customers’ service inclusion may increase/decrease secondary customers’ service inclusion and their experience of vulnerability. Moreover, secondary customers’ inclusion is often necessary to foster primary customers’ inclusion and well-being.

Fostering service inclusion and well-being for primary and secondary customers requires balanced inclusion and acknowledging the needs of both groups. Service providers may need to act as moderators within customer entities if discrepant needs occur.

The study addresses the under-researched areas of family members’ customer needs, their relation to primary customers’ needs, experiences of secondary vulnerability and context-related vulnerability.