Search results

The aim of this research is to explore how a programmatic multi-project context influences project-based firms (PBFs) in organizing their relations with other PBFs and suppliers in a project-based industry.

A multiple case study research is conducted. Data are collected from two case studies in the construction infrastructure sector. Eleven interviews with contractors and other suppliers are the primary source of data collection. The data are complemented by procurement documents and expert consultations.

The findings show that within a programmatic multi-project context, PBFs settle relations with (1) key partners for program management capacity, PBFs establish relations with (2) main contractors to divide projects and (innovation) tasks, and PBFs intensify relations with (3) suppliers to ensure continuity and expertise.

The study contributes to the body of project management literature by exploring PBF’s relations with other PBFs and suppliers in a multi-project context. Based on empirical data, the study provides a distinction in layers presenting distinct levels of PBF’s supplier relations. This layer structure provides an excellent starting point for future studies exploring the program perspective of PBFs in the integrated supply chain.

Given the increase in programmatic multi-project context for project-based domains, discussed in both literature and practice, this study explores the effect of programs on relations of PBFs with other PBFs and suppliers. The study distinguishes PBF’s relations with the different suppliers in three layers and discusses the characteristics of these relations.

This study aims to estimate the amount of money laundering (ML) with multiple proxy approaches and measure the effects of ML on various indicators of the economic and financial sectors. Theoretical justifications are recruited from the parasite theory of organised crime.

A quantitative research methodology was used on a balanced panel data set to test the study’s hypothesis through generalised method of moment (GMM). The study sample consisted of 77 countries, and the data was collected for 15 years (2005–2019).

A study has found that 1.23% of global gross domestic product is laundered yearly, and there is no noticeable decline in ML activities. Further study has also found that ML has devastating effects on countries, government revenue, foreign investment, economic development, political and peace conditions, bank liquidity, interest rate volatility and exchange rate volatility. The study has not witnessed the negative consequence of ML on countries’ inflation rates.

Estimates of the study guide policymakers about the volume of resources fleeing and helps them to decide the level of response needed. Further findings help them prioritise the response system according to the area most affected.

This study is an original contribution by the authors and has studied the effects of ML by computing the amount of ML by four different proxies.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This paper aims to conduct a bibliometric analysis of studies focusing on green supply chain management (GSCM) within the context of the digital economy.

We utilize the Web of Science database to search and filter relevant documents spanning the years 2003–2022. This extensive dataset enables us to analyze the growth and cutting-edge developments in research pertaining to GSCM in the digital economy.

The paper finds a significant increase in research interest and output, particularly noticeable from 2016 onwards, indicating the growing relevance of integrating GSCM with digital technologies. It is found that the prominent contribution of countries like China, England and the USA, underscoring a strong geographical diversity in research outputs. China leads in the number of publications, which reflects its significant role in shaping the discourse around GSCM in the digital economy. However, when it comes to citations, the USA leads, suggesting a higher impact or quality of research emanating from this region. Collaborative dynamics outlined in the study demonstrate extensive international cooperation, primarily among leading research countries, which is facilitated by shared digital platforms enhancing the research’s reach and impact. The study also highlights a range of emerging themes such as the adoption of blockchain technology, Internet of Things (IoT) and the circular economy within GSCM, indicating dynamic areas for future research.

The findings of this study hold significant practical implications for researchers, practitioners and policymakers. They shed light on the current state of research in GSCM within the digital economy, highlighting areas where further investigation is needed and pointing to the emerging trends in this field. Understanding the distribution of research and influential authors can guide future collaborative efforts and inform decision-making processes in the pursuit of sustainable supply chain practices in the digital era.

This paper contributes to the existing body of knowledge by providing a comprehensive bibliometric analysis of the evolving landscape of GSCM in the digital economy. It offers valuable insights into the growth patterns, key contributors and geographical distribution of research in this domain. This information is crucial for researchers and stakeholders seeking to stay at the forefront of sustainable supply chain practices in an increasingly digital world.

This study investigates barriers to consumers’ organic food purchasing. It identifies the factors and the extent to which they influence their purchase behaviours and future purchase intentions (i.e. continuance purchase intentions).

It combines qualitative and quantitative methods across two phases. Longitudinal research was carried out in two phases. It involved a thematic analysis and a covariance-based structural equation modelling approach. During Phase-1 and Phase-2, responses were collected from 376 and 351 respondents, respectively.

Phase 1 found the value barrier was significantly affecting the consumers’ purchase intention, while Phase 2 identified the impacts from both image and value barriers on purchase intentions. Notably, purchase intention affected continuance intention in both phases, while ethnocentrism showed no influence.

Using the innovation resistance theory, this study sheds light on the factors that prevent purchase intention. It offers valuable insights for policymakers and for the marketers of organic foods. This contribution implies that value and usage barriers were affecting the consumers’ purchase intentions in the short as well as in the long term. In sum, it suggests that consumers were not purchasing organic food as they felt it was either overpriced, not available in the market or because they were sceptical about its organic labelling.

This study aims to understand how distributed agile teams (DATs), encouraged by globalisation, and recently accelerated by the COVID-19 outbreak, adopt agile practices to achieve project goals by working virtually.

This study developed a multiple-case study involving four companies undergoing several changes, especially during the COVID-19 pandemic. The authors collected data mainly through in-depth, face-to-face interviews with seven key informants. Moreover, this study operates data triangulation by referring to secondary data sources and developing a grounded theory data analysis.

The findings highlight three main categories associated with the DAT functioning, namely, “DATs’ implementation issues”, “elements supporting DATs’ implementation” and “outcomes of DATs’ implementation”, that show DATs’ primary triggers, critical aspects and supportive actions for team functioning.

This paper produced valuable theoretical knowledge of DATs’ dynamics within a socio-technical approach that distinguishes soft and hard variables supporting DAT implementation. Moreover, the evidence provides useful suggestions for managers about creating an objective-oriented virtual work environment based on DATs’ self-organisation, digitally shared leadership and occasional on-site socialisation.

This paper provides new and interesting insights that bring to evidence the main variables related to DATs’ adoption and dynamics, showing supporting activities that enhanced their operativity. It provides a valuable descriptive framework for academics and practitioners to understand DATs’ functioning better and take action to improve their implementation.

This study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market.

The authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity.

The Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest.

Results reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market.

The experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively.

This study aims to develop the understanding of learning processes related to the new ways of interacting in the enforced digital workplace over time.

A multiple, longitudinal case study of knowledge-based workers in three firms located in Sweden has been conducted from March 2020 to March 2023. In total, 89 interviews with 32 employees in three knowledge-based firms have been collected.

The study shows how the intricate interaction between rules and norms for interaction and work must be renegotiated as well as un- and relearned when the physical work environment no longer frames the work context. Furthermore, technology can be viewed as both an enable and a barrier, that is, technology has enhanced collaboration between organizational members yet also created social difficulties, for example, related to communication and interaction. The study emphasizes that individuals learned through trial and error. That is, they tried behaviors such as translating social interactions" to a digital arena, appraised the outcomes and modified the practices if the outcomes were poor.

The present study does have several limitations. First, it is based on interviews with respondents within three organizations in Sweden. To broaden and deepen the understanding of both organizational and learning, future studies can contribute by studying other contexts as well as using a mixed method approach in other countries.

Results from the study can provide a practical understanding of how the rapid change from working at the office to working from home using digital technologies can be understood and managed.

Contributions include combining interaction order and un- and relearning among organizational employees. This insight is important given that the rapid digital transformation of our society has changed how work is performed and how the future workplace will be both structured and organized.

To investigate the role of domestic and foreign economic policy uncertainty (EPU) in driving the corporate bond yields in emerging markets.

The study utilizes monthly data from January 2008 to June 2023 from the selected emerging economies. The data analysis is conducted using univariate, bivariate and multivariate statistical techniques. The study includes bond market liquidity and global volatility (VIX) as control variables.

Domestic EPU has a significant role in driving corporate bond yields in these markets. The study finds weak evidence to support the role of the USA EPU in influencing corporate bond yields in emerging economies. Domestic EPU holds more weight and influence than the EPU originating from the United States of America.

The findings provide useful insights to policymakers about the potential impact of policy uncertainty on corporate bond yields and enable them to make informed decisions regarding economic policies that maintains financial stability. Understanding the relationship between EPU and corporate bond yields enables investors to optimize their investment decisions in emerging market economies, opens the scope for further research on the interaction between EPU and volatility and other attributes of fixed income markets.

Focuses specifically on the emerging market economies in Asia, providing an in-depth analysis of the dynamics and challenges faced by these countries, Explores the influence of both domestic and the USA EPU on corporate bond yields in emerging markets, offering valuable insights into the transmission channels and impact of EPU from various sources.

Universities strategically organize themselves around sustainability, including transformative goals in teaching and learning. Simultaneously, the role of online education has become more prominent. This study aims to better understand the purpose and process of creating online sustainability education (OSE) and to identify challenges and opportunities for implementing these courses and programs to achieve universities’ broader transformative sustainability goals.

This study uses a multiple case study design to research three universities in diverse geographical and institutional contexts (Finland, the USA and Colombia). Qualitative data was collected by interviewing program-related experts (n = 31) and reviews of universities’ strategic documents.

The findings suggest that despite important advances, further attention is merited regarding aligning the purpose of OSE with student learning outcomes, clarifying the values underlying the process of online program/course creation and developing transformative and process-oriented approaches and pedagogies to implement OSE. The authors also highlight emerging challenges and opportunities in online environments for sustainability education in different institutional contexts, including reaping the benefits of multilocality and diverse student experiences regarding sustainability issues.

There is a paucity of studies on university-level sustainability education in relation to online environments. This research expands on the existing literature by exploring three different geographical and institutional contexts and shedding light on the relationship between the practical implementation of OSE and universities’ broader sustainability goals.