Search results

Insufficient productivity development in the global and Finnish infrastructure sectors indicates that there are challenges in genuinely achieving the goals of resource efficiency and digitalization. This study adapts the approach of capability maturity model integration (CMMI) for examining the capabilities for productivity development that reveal the enablers of improving productivity in the infrastructure sector.

Civil engineering in Finland was selected as the study area, and a qualitative research approach was adopted. A novel maturity model was constructed deductively through a three-step analytical process. Previous research literature was adapted to form a framework with maturity levels and key process areas (KPAs). KPA attributes and their maturity criteria were formed through a thematic analysis of interview data from 12 semi-structured group interviews. Finally, validation and refinement of the model were performed with an expert panel.

This paper provides a novel maturity model for examining and enhancing the infrastructure sector’s maturity in productivity development. The model brings into discussion the current business logics, relevance of lifecycle-thinking, binding targets and outcomes of limited activities in the surrounding infrastructure system.

This paper provides a new approach for pursuing productivity development in the infrastructure sector by constructing a maturity model that adapts the concepts of CMMI and change management. The model and findings benefit all actors in the sector and provide an understanding of the required elements and means to achieve a more sustainable built environment and effective operations.

Introduction: Sustainability and Industry 4.0 have recently influenced the global economy. With the Industrial Revolution 4.0, there has been a significant focus on digital sustainability in enterprises. Micro, small and medium enterprises (MSMEs) are the most vulnerable sections regarding new transformations.

Purpose: Even during the COVID-19 pandemic, when all businesses had no option other than to adopt digitisation, MSMEs faced tremendous issues to make this shift. Despite the immense focus on digital sustainability, some deterrents exist to its adoption in MSMEs. Contemporary research focuses on determining the critical deterrents to digital sustainability in MSMEs.

Methodology: This chapter employs the interpretive structural modelling technique, and Matrice impacts Croises Multiplication Appliqué a classement (MICMAC) analysis to identify and further classify the deterrents to digital sustainability in MSMEs.

Findings: Legal barriers and firms’ economic conditions are identified as the major deterrents. Eliminating these deterrents would help minimise the minor ones too. To deliver global sustainability goals, digital sustainability must be duly adopted everywhere.

In recent years, the robotic process automation (RPA) technology has increasingly been used to automate business processes. While a lot of research has been published on the potential and benefits of the technology, only a few studies have conducted research on challenges related to RPA adoption. Hence, this study aims to identify and discuss challenges related to RPA implementation projects.

Following an inductive methodology, interviews have been conducted with consultants who were involved in multiple RPA implementation projects. Hence, their extensive experience and views contribute to a detailed and in-depth understanding of the phenomena under research.

The results suggest that there are various process-related, technical, resource-related, psychological and coordinative challenges that must be considered when conducting an RPA implementation project.

This paper contributes to knowledge by presenting a new typology of challenges, as well as providing an in-depth discussion of the individual challenges that organizations face.

Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.

A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.

This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.

This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.

The paper aims to understand why and how paratextual elements are included in annual reports, hence how meaning is made through the workings of language and imagery.

A comprehensive framework of analysis, combining Genette’s paratextual elements with Barthes’ rhetoric and denotation and connotation concepts, is applied to the case study of the dstgroup, a Portuguese engineering and construction group.

The study demonstrates the potential of the annual report as a communication tool between an organisation and its stakeholders. The framework of analysis evidences that the paratextual elements highlight and supplement accounting information and that the denotative and connotative meanings associated with them make visible and enhance intangible features of the organisation.

The paper extends theories from other interdisciplinary fields to accounting communication and proposes a comprehensive framework that combines the writings of Genette and Barthes. By exploring the Portuguese under-researched context, it also adds to the literature by analysing the rationales and choices of the preparers on the inclusion of paratextual elements in annual reports.

Este artículo pretende entender por qué y cómo se incluyen elementos paratextuales en los informes anuales y, cómo se construye el significado a través del funcionamiento del lenguaje y las imágenes.

Se desenvolvió un marco de análisis global, que combina los elementos paratextuales de Genette y los conceptos de retórica y denotación y connotación de Barthes. Este marco se aplicó al estudio de caso del grupo dst, un grupo portugués de ingeniería y construcción.

El estudio muestra el potential del informe anual como herramienta de comunicación entre una organización y sus grupos de interés. El marco de análisis evidencia que los elementos paratextuales resaltan y complementan la información contable y que los significados denotativos y connotativos asociados a ellos hacen visibles y realzan rasgos intangibles de la organización.

Este artículo extiende teorías de otros campos interdisciplinarios a la comunicación contable y propone un marco global que combina los escritos de Genette y Barthes. Explorando el contexto portugués, insuficientemente investigado, también se añade a la literatura mediante el análisis de las motivaciones y opciones de los preparadores de los informes anuales sobre la inclusión de elementos paratextuales.

Digital transformation is a process in which organizations use technology as an essential resource to improve performance and increase market reach and results.

This research aims to verify how dynamic capabilities are configured for the development of strategies and processes necessary for digital transformation. To this end, qualitative research of an exploratory nature was carried out, operationalized through in-depth individual interviews with a semi-structured approach with managers who occupy leadership positions, in addition to a documentary survey of the internal records of the analyzed organization. In data analysis, the content analysis technique was used.

The main results of the research showed that the dynamic capabilities configured in sensing, seizing and reconfiguring contribute to leveraging digital transformation in the business studied through the reformulation of strategies to integrate and coordinate the implementation of new routines, investments in technological and human resources and actions to change culture with a focus on digital transformation considering the company’s stakeholders.

This transformation affects business models, products, services and processes, as well as the review of strategies, organizational structures and management concepts.

Purpose: The purpose of this chapter is to identify various factors that may influence the adoption of online education by students in higher education institutions (HEIs) and contribute to the achievement of sustainable development goals (SDGs) post-COVID-19.

Need for the Study: The study addresses the need to ensure inclusive and equitable quality education, as emphasised by SDG4. It focuses on lifelong learning opportunities and aims to understand the impact of online education on students in HEIs. By identifying the factors that influence adoption, the study aims to contribute to the development of effective strategies for promoting online education.

Methodology: The study utilises a framework incorporating the use of Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) software. The framework allows for the analysis of the collected data’s reliability, validity and adequacy.

Findings: The study’s findings indicate that providing students with affordable online higher education can facilitate skill enhancement and create job opportunities. These findings highlight the potential of online education in contributing to the achievement of SDGs, particularly SDG4.

Practical Implications: The practical implications of the study suggest that promoting affordable online higher Education can have a positive impact on students, enabling them to acquire new skills and access job opportunities. By embracing online education, institutions can contribute to the advancement of SDGs and ensure inclusive and equitable quality education for all.

This study aims to investigate the consequences of middle managers’ sensegiving for organisational change in neglected workplaces, where middle managers are given insufficient resources because of receiving low attention from top management.

The authors conducted a case study of three call centres in the Japanese non-life insurance industry. To collect data, the authors conducted interviews with ten stakeholders and made multiple field observations.

The authors identified the following mechanism: in neglected workplaces, middle managers initially focus on sensegiving to employees because they recognise the difficulty of eliciting support from top management. However, as a result, they see sensegiving to employees as top priority and do not try to elicit the support of top management, which is necessary for further organisational change. As a result, organisational change stops at a certain level.

The authors identified the following mechanism: in neglected workplaces, middle managers initially focus on sensegiving to employees because they recognise the difficulty of eliciting support from top management. However, as a result, they see sensegiving to employees as their top priority and do not try to elicit the support of top management, which is necessary for further organisational change. As a result, organisational change stops at a certain level.

First, this study contributes to the body of research on the effects of sensegiving on organisational change. It shows the new problems hidden behind organisational change, which existing research merely regards as independent successes. Second, this study identifies middle managers’ behaviour during organisational change in neglected workplaces. Instead of focusing on the factors necessary for successful organisational change, as in existing studies, this study extends the knowledge of the role of middle managers in organisational change by focusing on their behaviours when success factors are not aligned.

Due to the influence of complex and intersecting factors, self-governed public open spaces (POSs) (managed by local communities) are subject to collective action dilemmas such as tragedy of the commons (overexploitation), free-riding, underinvestment and mismanagement. This review paper adopts a multi-dimensional and multi-tier social-ecological system (SES) framework proposed by McGinnis and Ostrom, drawing on collective action theory to explore the key institutional-social-ecological factors that impact POS self-governance.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was utilized to systematically screen and review the relevant literature for the period from 2000 to 2023 in three databases: Web of Science, Scopus and Google Scholar. A total of 57 papers were chosen for in-depth analysis.

The literature review identified and categorized several variables associated with the self-organizing system of POS; consequently, an SES-based POS management framework was developed for the first time, consisting of 114 institutional-social-ecological sub-variables from different dimensions and three levels. Compared to ecological factors, among others, governance organizations, property-rights systems, socioeconomic attributes and actors' knowledge of SES have been commonly and primarily studied.

There is still room for the refinement of the conceptual SES-based POS collective action framework over the time (by adding in new factors), and indefinitely empirical research validating those identified factors is also worth to be undertaken, particularly testing how SES factors and interaction variables affect the POS quality (collective action).

The findings of this study can provide local policy insights and POS management strategies based on the identification of specific SES factors for relevant managers. Moreover, this research makes significant theoretical contributions to the integration of the SES framework and collective action theory with POS governance studies.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.