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Previous studies have produced inconsistent findings regarding the effects of resource constraints on corporate illegal behavior. This study aims to explore how entrepreneurial firms can overcome the difficulties generated by resource constraints.

Drawing on insights from general strain theory and focusing on listed entrepreneurial firms, this study proposes that failure to obtain enough resources through listing generates strain in the managers of listed entrepreneurial firms, driving them to resort to corporate financial fraud as a solution. Nevertheless, such relationships between resource constraints and the likelihood of corporate financial fraud can be weakened by innovation capability, because innovation capability can generate more confidence in their managers and relieve their strains, thereby dissuading them from engaging in corporate financial fraud.

According to our empirical results, both financial and human resource constraints are positively related to the likelihood of corporate financial fraud in listed entrepreneurial firms, but such effects can be mitigated by innovation capability.

This study provides practical implications for both regulators and managers by indicating that although entrepreneurial firms with resource constraints are more likely to commit financial fraud, innovation capability could be a strategic approach to enhance managers’ confidence and relieve the strain.

Our study contributes to the literature by enriching our understanding of the consequences of resource constraints in entrepreneurial firms and highlighting the strategic importance of innovation capability in mitigating such effects.

Maintaining a healthy and productive workforce is a challenge for most organizations. This is even truer for health organization, facing staff shortages and work overload. The aim of this study is to identify the resources and constraints that influence managers' mental health and better understand how they are affected by them.

A qualitative approach was chosen to document the resources, the constraints as well as their consequences on managers in their day-to-day realities. The sample included executive-, intermediate- and first-level managers from a Canadian healthcare facility. A total of 62 semi-structured interviews were conducted. The coding process was based on the IGLOO model of Nielsen et al. (2018) to which an employee-related level was added (IGELOO).

Results highlight the importance of considering both resources as well as constraints in examining managers' mental health. Overarching context, organizational constraints and the management of difficult employees played important roles in the stress experienced by managers.

The results offer a better understanding of the importance of intervening at different levels to promote better organizational health. Results also highlight the importance of setting up organizational resources and act on the various constraints to reduce them. Different individual strategies used by managers to deal with the various constraints and maintain their mental health also emerge from those results.

In addition to addressing the reality of healthcare managers, this study supplements a theoretical model and suggests avenues for interventions promoting more sustainable organizational health.

This paper proposes an innovative intelligent simulation-based construction planning framework that introduces a new approach to simulation-based construction planning.

In this approach, the authors developed an ontological inference engine as an integrated part of a constraint-based simulation system that configures the construction processes, defines activities and manages resources considering a variety of requirements and constraints during the simulation. It allows for the incorporation of the latest project information and a deep level of construction planning knowledge in the planning. The construction planning knowledge is represented by an ontology and several semantic rules. Also, the proposed framework uses the project building information model (BIM) to extract information regarding the construction product and the relations between elements. The extracted information is then converted to an ontological format to be useable by the framework.

The authors implemented the framework in a case study project and tested its usefulness and capabilities. It successfully generated the construction processes, activities and required resources based on the construction product, available resources and the planning rules. It also allowed for a variety of analyses regarding different construction strategies and resource planning. Moreover, 4D BIM models that provide a very good understanding of the construction plan can be automatically generated using the proposed framework.

The active integration between BIM, discrete-event simulation (DES) and ontological knowledge base and inference engine defines a new class of construction simulation with expandable applications.

Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).

A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.

Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.

It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.

An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

The purpose of this paper is cost optimization of project schedules under constrained resources and alternative production processes (APPs).

The model contains a cost objective function, generalized precedence relationship constraints, activity duration and start time constraints, lag/lead time constraints, execution mode (EM) constraints, project duration constraints, working time unit assignment constraints and resource constraints. The mixed-integer nonlinear programming (MINLP) superstructure of discrete solutions covers time–cost–resource options related to various EMs for project activities as well as variants for production process implementation.

The proposed model provides the exact optimal output data for project management, such as network diagrams, Gantt charts, histograms and S-curves. In contrast to classic scheduling approaches, here the optimal project structure is obtained as a model-endogenous decision. The project planner is thus enabled to achieve optimization of the production process simultaneously with resource-constrained scheduling of activities in discrete time units and at a minimum total cost.

A set of application examples are addressed on an actual construction project to display the advantages of proposed model.

The unique value this paper contributes to the body of knowledge reflects through the proposed MINLP model, which is capable of performing the exact cost optimization of production process (where presence and number of activities including their mutual relations are dealt as feasible alternatives, meaning not as fixed parameters) simultaneously with the associated resource-constrained project scheduling, whereby that is achieved within a uniform procedure.

The drum‐buffer‐rope/buffer management approach radically changes the content of production management practice, research and pedagogy. Documents the major impacts that drum‐buffer‐rope has on production management study and practice. In summary it provides a framework which distils the complexities of material flow into an understandable format; reduces drastically the number of resources that must be explicitly scheduled; warns of potential disruption to the production plan; controls lead time; guides continuous improvement efforts; offers a significantly improved alternative to the kanban production system; aligns local resource performance measures with global organizational performance; and makes traditional job shop capacity management techniques obsolete.

Scholars have studied how entrepreneurs acquire resources but have not examined how resources may be bundled with constraints, which can threaten entrepreneurial autonomy. Organizational sponsors, such as incubators and accelerators, provide entrepreneurs with resources, but how do entrepreneurs sustain autonomy while seeking resources and support? We studied five entrepreneurial firms in a business incubator over a six-month period. While benefitting from incubator resources, entrepreneurs also experienced unexpected constraints, including mentor role conflict, gatekeeper control, and affiliation dissonance. By showing how entrepreneurs unbundled the incubator’s resources from constraints, we explain how entrepreneurs manage the tension between acquiring resources and preserving autonomy.

The purpose of this paper is to explore the benefits of integrating the theory of constraints (TOC), resources-based theory (RBT), resource advantage theory (RAT), with a structured project-based methodology e.g., Project Management Body of Knowledge. This paper describes each theory and explores what benefits a unified model would bring to project management. This paper represents the conceptual development of an integrated framework that will be tested in a range of project management scenarios in various industrial sectors.

Extant literature is used to develop a conceptual framework of an integrated model that will be tested in the field for robustness. The model has been applied to published projects to identify its strengths and weaknesses.

The work shows important implications for improved success of projects from the use of TOC, RAT and resource dependence theory (RDT). Specifically, it emphasizes the need for application of strategic theories to project management.

While TOC, RAT and RDT are well established in the context of organization theory, there is limited application in project management. Moreover, the model has yet to be applied in the field. The hypotheses identified in this research are currently being tested using field-based surveys.

The research falls short in addressing some resources, e.g. innovation, tacit knowledge and decision making methods in traditional project management context. Therefore, identifying these critical resources in future work and exploiting them as the means of improving project performance would enhance the success of project-based management.

Project management is an emergent discipline and a project is temporary in nature. Therefore, new ideas and development of theories for project management practices are required. This innovative research, for example, may change the way projects are executed in future.

This paper examines the components of a successful project according to the iron triangle, i.e. scope, quality, time and cost. However, through the application of TOC, RAT and RDT into an integrated project-based management framework gives new insights to resources management.

The purpose of this research is to develop a time-cost optimization model to schedule repetitive projects while considering limited resource availability.

The model is based on the constraint programming (CP) framework; it integrates multiple scheduling characteristics of repetitive activities such as continuous or fragmented execution, atypical activities and coexistence of different modes in an activity. To improve project performance while avoiding inefficient hiring and firing conditions, the strategy of bidirectional acceleration is presented and implemented, which requires keeping regular changes in the execution modes between successive subactivities in the same activity.

Two case studies involving a real residential building construction project and a hotel refurbishing project are used to demonstrate the application of the proposed model based on four different scenarios. The results show that (1) the CP model has great advantages in terms of solving speed and solution quality than its equivalent mathematical model, (2) higher project performance can be obtained compared to using previously developed models and (3) the model can be easily replicated or even modified to enable multicrew implementation.

The original contribution of this research is presenting a novel CP-based repetitive scheduling optimization model to solve the multimode resource-constrained time-cost tradeoff problem of repetitive projects. The model has the capability of minimizing the project total cost that is composed of direct costs, indirect costs, early completion incentives and late completion penalties.

The purpose of this paper is to develop and demonstrate an outsourcing model in which constraint resource prevents the throughput of the organization.

The paper proposes an integrated model by combining the Hurwicz criterion, the theory‐of‐constraints (TOC) and linear programming (LP) into a single evaluation model in a multi‐product constraint resource environment. A case study is presented to demonstrate the effectiveness of this model. The decision model compares four alternatives: standard cost accounting, standard TOC, LP analysis, and our own solution, which is an approach that combines TOC, LP, and the Hurwicz criterion.

The numerical results show that this model is superior and more realistically optimizes resource allocation and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This is the first time that the integrated model comprising of Hurwicz‐TOC‐LP model has been used to maximize the product throughput. Instead of calculating $ return per constraint minute, this method decides the priority of product and resource center that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing organization where one can compare the financial performance of the organization by selecting the right decision model.