Search results

This paper aims to explore how the time and temporal aspects are managed in a financial accounting outsourcing (FAO) transition process in an international interorganizational context. As a research outcome, the authors identify management interventions of both the service provider (SP) and the outsourcing company (OC) at both the corporate and operational levels.

The framework by Huy (2001a, 2001b) was used to analyze the qualitative data, which draw on observations, participation in 32 official meetings during the outsourcing process, informal discussions with key actors from the SP and the OC, and archival data of a single case company.

The authors illustrate how the time and temporal aspects of planned accelerated change are managed through management interventions during the FAO transition process. All four ideal intervention types (commanding, engineering, teaching and socializing) were used sequentially but also jointly to complement one another. The pacing was mostly rapid, owing to strong commanding interventions initiating almost every stage. When analyzing the FAO transition process, the authors identified four stages: contact, contract, convergence and control. Moreover, the authors focused on the role of the operational-level managers and accounting specialists of both organizations. The findings indicate that management interventions vary with the management level.

This study contributes to the interorganizational control literature by considering the time and temporal aspects in planned organizational change and the role of operational-level managers in managing large-scale changes.

This study aims at understanding how higher education institutions (HEIs) can contribute to sustainable development, by designing their programmes for bringing about a transformative impact on communities and students, and also to examine what alternative pedagogical approaches could be used for that. In the past decades, HEIs have increasingly created social innovation (SI) programmes, as a way to achieve United Nations Sustainable Development Goals. These community-oriented and field-based programmes are difficult to ally with conventional classroom education. This study explores how these programmes could integrate the participatory approach and what would be the benefits. It also investigates the effectiveness of the experiential learning approach for teaching sustainability.

A case study method is used to document SI projects initiated by an HEI programme in rural India.

It was found that the participatory approach contributes to empowering communities and also benefits the students in terms of academic, professional and personal growth. Empirical findings show that experiential learning is an efficient method to teach sustainability. Ultimately, both pedagogical approaches are found to be mutually beneficial.

This study fills a gap in the literature, by providing empirical evidence on how HEI can implement innovative educational strategies such as participatory approach and experiential learning in their programmes towards teaching sustainability. A conceptual model for HEI interested in developing similar programmes is also proposed. To the best of the authors’ knowledge, this study is one of the first studies focusing on the context of Indian HEI.

This study builds on the panarchy theory by viewing the supply chain as a socio-ecological system and further expands it by considering the within-level linkages internal to the supply chain level. Three types of linkages are considered: the two cross-level linkages with the planetary and the political-economic levels and the supply chain within-level linkages. The research questions are addressed using the data gathered by the Carbon Disclosure Project within its Supply Chain Programme.

This work aims to study, applying the lens of panarchy theory, how the planetary and the political-economic levels affect the supply chain within-level linkages for sustainability. Furthermore, the difference in how these cross-level linkages influence focal firms and first-tier suppliers is explored.

The results show that considering the planetary-supply chain linkage, climate change risk exposure is likelier to foster within-level linkages with buyers than with suppliers. Further, climate change mitigation investments have different roles in the different tiers: focal firms are pushed to strengthen the linkages with their suppliers when they lose efficacy in improving their carbon performance, whereas first-tier suppliers exploit investments to gain legitimacy. Discussing the political-economic level effect, perceptions from first-tier suppliers could be two-fold: they could perceive a mandating power mechanism or exploit policymakers’ knowledge to advance their capabilities.

The results contribute to the sustainable supply chain management literature by providing empirical evidence of the cross-level linkages theorised by the panarchy theory. Moreover, the concept of within-level linkages is proposed to apply the theory in this field.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.