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Academy of Management Proceedings
No full textIn today’s changing world of work, an increasing group of individuals are actively taking charge of their careers inside or outside of organizations (Ashford et al., 2018; Parker & Collins, 2010). For these individuals, identity is a key resource that provides a structure to see and interpret the business context and to behave in it (Leary and Tangney, 2012; Markus, 1977). This symposium addresses identity for two specific groups that are currently receiving considerable attention in literature: organizational leaders and gig workers
Early alzheimer disease round table project: Preparedness of the Belgian health system
Get PDFDisease modifying therapies (DMT) in the field of Alzheimer’s disease becoming accessible will require a transformation of Belgian health care practice. Early diagnosis is a crucial first step for these therapies as the maximal benefit outcome is expected if treatment is started as early as possible. This health policy-preparing paper resulting from a Belgian Early AD Round Table, complemented by an anonymized memory clinics survey and a computer simulation, was geared to investigate the Belgian healthcare system infrastructural preparedness to receive a DMT in the field of Alzheimer disease, which represents a high unmet clinical and societal need. Key summary recommendations include; • Conducting an awareness campaign towards the broader public as of a DMT becoming available; • Increasing GP awareness of implementation guidelines of the early-AD care and diagnostic pathway stressing multi-professional collaboration on diagnostic strategies; • To expedite patient diagnosis and treatment by considering reimbursement of CSF analysis, regardless of their use in symptomatic treatment or –even more so– DMT-available contexts; • CSF analysis cost-effectiveness is shown to require transversal budget impact analysis considering societal costs; • In the long run, to redesign the Belgian Memory Clinics Convention to act as the guardian of a national uniform quality AD health service offering; • To organically grow the present memory clinic-based loco-regional approach to AD treatment, which would result into a higher number of memory clinics acting upon a revised DMT-based health service offering; • To invest cost-effectively in the competence and skills of the informal caregiver; • To set up industry-independent societally funded national AD & dementia registries characterized as care registries and diagnosis/syndrome-specific quality of care registries. Please also consult the recommendations following the public presentation of these study results under Chapter 7 – Conclusions and Recommendations.We acknowledge the financial support and global subject matter expertise of Biogen provided to conduct this health policy-preparing study geared to investigate the Belgian healthcare system infrastructural preparedness to receive a disease-modifying therapy in the field of Alzheimer disease, representing a high unmet clinical and societal need
An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem
No full textIn recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1,000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP.This work is supported in part by the scholarship from China Scholarship Council (CSC) under the Grant CSC No. 201708210261
The value of new customer metrics in the digital era for better understanding and predicting business performance
No full textDigital transformation is changing the landscape of the interaction among customers, firms and products. Customers and firms are moving from a brick-mortar setting to a digital space dimension. There is an increasing interest of scholars/practitioners to understand the mechanism and metrics of the interaction between customers and products in the digital era
Silence is not always golden: The impact of diversity and inclusion on auditor judgment
No full textAuditors responsibility: reasonable assurance that financial statements are free of material misstatement, whether caused by error or fraud. Audit team: composed hierarchically, creating surface-level diversity. How does hierarchical team diversity structure affect junior auditor’s conformity
Why middle managers struggle to implement DEI strategies
No full textTo successfully achieve DEI change, organizational leaders must understand the implementation challenges faced by middle managers and incorporate their specific needs into policy development. The authors identify two key tensions faced by middle managers — the autonomy vs. control tension, and the short-term vs. long-term tension — and offer strategies for leaders to help middle managers navigate them
Using Earned Value Management and Schedule Risk Analysis with resource constraints for project control
No full textThe main goal of project control is to measure the actual project progress such that the deviations from the plan can be identified and corrective actions can be taken to bring the project back on track. However, in resource-constrained projects, disrupted activities affect their successors due to precedence relations and the other activities due to resource constraints, both of which will result in deviations during project progress. Since the project control approaches solely focus on the deviations based on the network analysis, they do not accurately reflect the progress of resource-constrained projects. This paper extends project control approaches for resource-constrained projects to measure and evaluate whether the project progress is acceptable. Moreover, we design three scenarios considering possible resource conflicts to take corrective actions when needed. In the computational experiment, this project control process is applied to a large set of projects with different characteristics and further validated on real-life project data. The results show that the proposed scenarios and different project control approaches are efficient and reliable, but their use depends on project network structure and resource scarceness
Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management
No full textDeep reinforcement learning (DRL) has proven to be an effective, general-purpose technology to develop ‘good’ replenishment policies in inventory management. We show how transfer learning from existing, well-performing heuristics may stabilize the training process and improve the performance of DRL in inventory control. While the idea is general, we specifically implement potential-based reward shaping to a deep Q-network algorithm to manage inventory of perishable goods that, cursed by dimensionality, has proven to be notoriously complex. The application of our approach may not only improve inventory cost performance and reduce computational effort, the increased training stability may also help to gain trust in the policies obtained by black box DRL algorithms
Manufacturing project scheduling considering human factors to minimize total cost and carbon footprints
No full textMake-to-order (MTO) or engineer-to-order (ETO) systems produce complex and highly customized products and, therefore, there is a need for advanced project scheduling approaches for production planning in these systems. An important aspect of production scheduling is the assignment of operators with specific human factors to activities in a manufacturing project. This assignment impacts the duration of the activities, the total wage cost of the project and even the energy consumption during production. With increasing concern regarding low-carbon production in manufacturing, the human factors of operators thus cannot be ignored in the decision-making process in production project scheduling. In this context, our study considers an extension of the well-known resource-constrained project scheduling problem for manufacturing. This problem is represented as a bi-objective optimization problem with the conjoint objectives of minimizing the total cost of the project and its carbon footprint. Two variants of a genetic algorithm-based memetic algorithm (MA) are proposed to solve this problem and a set of artificial, realistic project instances are generated to evaluate the proposed solution procedure. Experimental results show that the proposed MA outperforms the well-known non-dominated sorting genetic algorithms (NSGA-II and NSGA-III) and its enhanced approach (ENSGA-II) in terms of both solution quality and computational efficiency. The experiments are conducted on both real-life case study data from an MTO project in the furniture industry and a large set of artificial data instances. Our research allows project managers to select appropriate operators to execute activities based on human factors, wage and power consumption with the objectives of minimum total cost and carbon footprint