Search results

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.

This study aims to analyse the influence of digitalisation on the management control function of small and medium-sized enterprises (SMEs). In particular, it aims to illuminate how digitalisation influences management control elements, organisation and roles/competencies and to identify obstacles to digitalisation of management control in SMEs and measures taken to overcome them.

The study is based on guideline-supported expert interviews conducted with 14 financial managers from SMEs in Germany, Austria and Switzerland.

This study reveals the influence of digitalisation on management control elements, organisation, and roles/competencies. The automation and standardisation of management control processes result in new elements for management control, such as strategic support for management. In addition, the increased availability and transparency of data enable the use of instruments within a company that allow for quick analyses of the company's development. Digitalisation leads to the integration of management control into the corporate network and, thus, a change in the organisation of management control. It also triggers the expansion of management control competencies, especially IT competencies. A shortage of internal digitalisation resources, unclear corporate roadmaps, and a lack of managerial experience loom as central challenges for digitalising the management control function. Measures derived from the interviews can help SMEs overcome the obstacles to the digitalisation of management control.

This research is the first interview-based study of the impact of digitalisation on management control in SMEs, potential obstacles to that digitalisation, and measures to overcome those obstacles. Thus, it contributes to the emerging debate on factors that may explain why SMEs lag in terms of the digitalisation of their internal processes.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Growth optimism, which describes the expected future growth of a firm, is an important but underexplored construct in strategy. This paper aims to assess the planning antecedents of such growth optimism by using a large Australian sample of small enterprises.

The authors use a secondary data set, gathered among Australian small to medium enterprises (SMEs), by the Australian Bureau of Statistics (ABS). The analysis adopts a regression approach including a mediated and a non-mediated path to explore the direct and indirect effects of strategic planning and budgetary planning and management on expected future revenues.

This paper assesses the implications of concurrent strategic planning and financial management dynamic capabilities on anticipated future revenue growth, an important predisposition dynamic capability. The authors note that this configuration of actions and predisposition aligns closely with the necessary requirements for growth. The findings suggest that firms that use strategic planning and robust budget planning and monitoring processes exhibit higher optimism about future sales growth and firms that effectively configure these planning activities with market development tend to exhibit higher growth and more growth optimism.

In terms of theoretical contributions, the paper strongly supports the formality view in the formal/informal debates associated with effectuation strategies. The authors suggest that appropriate strategic and budgetary planning and control systems act as a counterbalance to organisational confusion and managerial capriciousness, leading to improved confidence among managers and their employees regarding future resource commitments and plans.

The findings of the paper are potentially important for both managers and policy makers. For managers seeking to grow their future sales, planning is shown to be an important antecedent activity. The presence of financial and strategic planning may predispose firms to make important investment decisions that drive future growth. Also, a better understanding of the firm’s current and future strategic and financial position may be evidence of effective firm management, a situation that, in turn, drives growth.

In terms of social and policy implications, the data gathered for the survey by the ABS forms a valuable collection of information in relation to business practices. Australian firms are required by law to regularly report budget plans and outcomes. The research suggests that this data can inform policy initiatives, particularly in relation to programmes that may assist small and young firms to undertake prospective strategic and budgetary planning.

To the best of the authors’ knowledge, this is the first paper to investigate the particular configuration of strategic and financial planning and anticipated sales growth in the SME context.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

This paper aims to critically examine the accounting and information systems literature to understand the changes that are occurring in the management accounting profession. The changes the authors are interested in are linked to technology-driven innovations in managerial decision-making and in organizational structures. In addition, the paper highlights research gaps and opportunities for future research.

The authors adopted a grounded theory literature review method (Wolfswinkel et al., 2013) to achieve the study’s aims.

The authors identified four research themes that describe the changes in the management accounting profession due to technology-driven innovations: structured vs unstructured data, human vs algorithm-driven decision-making, delineated vs blurred functional boundaries and hierarchical vs platform-based organizations. The authors also identified tensions mentioned in the literature for each research theme.

Previous studies display a rather narrow focus on the role of digital technologies in accounting work and new competences that management accountants require in the digital era. By contrast, the authors focus on the broader technology-driven shifts in organizational processes and structures, which vastly change how accounting information is collected, processed and analyzed internally to support managerial decision-making. Hence, the paper focuses on how management accountants can adapt and evolve as their organizations transition toward a digital environment.

This study aims to explore how managers exercise agency in strategic investment decisions (SIDs) by drawing on their knowledgeability of the strategic context. Specifically, the authors address the role of position–practice relations and irresistible causal forces in this conduct.

The authors examine SID-making (SIDM) practices in four case organisations operating in highly competitive markets, conducting interviews with managers at various levels and analysing company documents. Drawing on strong structuration theory, the authors show how managerial decision makers draw upon their knowledge of organisational context when exercising agency in SIDs.

The authors provide insights into how SIDM behaviour, specifically agents’ conduct, is shaped by a combination of position–practice relations and the agents’ comprehension of their organisation’s context.

The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice.

The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice. Particularly, the authors contribute to this literature by identifying irresistible causal forces and illuminating why actors might not resist in SIDM processes, despite having the potential to do so.

A major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.

Using a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.

We find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.

The study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.

The results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.

This is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

Apples have always been considered a healthy product able to provide curative properties to consumers. In Italy, there is a long tradition of apple consumption and production both as a fresh product and as processed food. However, as with many other products, the consumption of fruits and vegetables and, more specifically apples, has been drastically affected by the first lockdown in 2020. In this project, the authors investigate whether the change in consumption habits had long-lasting consequences beyond 2020 and what are the main eating motivations, food-related behavior and socio-demographic affecting the consumption of fruits and vegetables after the pandemic.

The authors ran two online surveys with 1,000 Italian consumers across a year (from October 2021 to December 2022). In the study, participants answered questions about their consumption habits and their eating motives. Out of 1,000 consumers, the authors included in the final analysis only the participants who answered both surveys, leaving a final sample of 651 consumers.

The results show that participants have allocated more budget to fruit and vegetables after the lockdown than before it. Moreover, consumers reported an average increase in the consumption of apples. However, the increase was more pronounced for people aged between 30 and 50 years old and identified as female. After showing the difference across time, a cluster analysis identified three main segments that differ in their eating motives, place of purchase and area of residence.

Overall, the results contribute to a better understanding of how the global pandemic is still affecting people's daily life. Moreover, the findings can be used to guide the marketing and communication strategies of companies in the food sector.

To the best of the authors' knowledge, this is the first study that investigates changes in the consumption of fruits and vegetables, and, more specifically, apples, in Italy more than one year after the beginning of the COVID-19 pandemic. Moreover, the study proposes a classification of consumers based on their habits in a time frame during which the COVID-19 wave was at its bottom which is not currently present in the literature.