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This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

This introduction provides the methodological framework for the book, approaching the business of football through the lens of its most reliable consumers – the fanbase. Fan cultures necessarily inform the normative understanding of a football club, due to the popularly held belief that it is the fan’s – or some reified idea of the fan – that is the permanent feature of a football club and that provides its identity. Players and owners come and go, but the relationship between the club and the fan is, theoretically, never-ending. In truth, this is never a real fan who could exist, but a constructed image of the fan built out of other narratives and that, at some level, football fans associate themselves. This fan is no one in particular, but is drawn from a close reading of football culture and identifying the directives of the traditional fan. Utilising a combination of critical theory and the existing literature on football club ownership, our goal is to reveal the distinction between how people talk about the social dimension of football clubs, and how they actually relate to their fans and the wider world in the era of late capitalism. A club is not simply the romanticised notions held by those within the games, but, as with all businesses, it is also the product of it conducts itself in a series of other networks of exchange. Often irreconcilable with the aforementioned romantic notions, these networks often get hidden by the prevailing discourse.

Major maintenance projects are often regarded as maintenance activities regardless of the projects' complexity and scale. Consequently, very scarce research attention has hitherto been paid to the critical skills required when undertaking these projects. More specifically, the body of relevant knowledge is deprived of a study focusing on maintenance projects within the energy sector. In view of this shortcoming, this research aims to examine the critical project management (PM) skills required to deliver major maintenance projects within the energy sector.

Based on a quantitative research strategy, this study addressed the knowledge gap through a cross-sectional survey of professionals involved in the delivery of major maintenance projects in the United Kingdom's (UK) energy sector. Data obtained were analyzed via descriptive (e.g. frequencies, mean and standard deviation [SD]) and inferential statistical analyses (One sample t-test and exploratory factor analysis (EFA)).

Out of the 45 PM skills identified in the literature and examined by the respondents, the results obtained from the One sample t-test (based on p (1-tailed) = 0.05) showed that 37 were considered to be at least “important,” accounting for 80.4% of all the skills identified. EFA revealed a clustering of the PM skills items into seven components: “skills related to work scheduling and coordination”; “communication, risk, safety and stakeholder management skills”; “quality assurance skills”; “people management skills”; “skills related to forecasting scope and duration of outage”; “implementation of processes and time management skills” and “technical/engineering skills and experience pertaining to the outage and local site knowledge.”

This study has identified and contributed to the limited state-of-the-art skills project managers must possess to manage major maintenance projects in the energy sector successfully. The findings would be useful to organizations within the energy sector in ensuring that the organizations have suitable personnel in place to deliver major maintenance projects on the organizations' assets.

Despite considerable research on the subject, there is still some misunderstanding about what characterizes successful project delivery in construction projects. Evaluating project delivery success is crucial for organizations since it enables them to prepare for future growth through more effective project management mechanisms and rank the organization's projects for continuous improvement. There is considerable disagreement over a set of success criteria that can be applied to all kinds of projects when evaluating project delivery success, making it a complicated procedure for practitioners and scholars. This research seeks to alleviate the problem by validating and testing a systematic project delivery success model (3D integration model) in the Australian construction industry. The aim is to establish a dependable approach built upon prior research and reliable in evaluating delivery success for any project type.

Based on a novel project delivery success model, this research applies a case study methodology to analyse 40 construction projects undertaken by a single Australian project management consultancy. The research utilizes a mixed-method research approach and triangulates three sets of data. First, the project delivery success (PDS) scores of the projects are calculated by the model. Second, a qualitative analysis targeting the performance of the same projects using a different system called the performance assessment review (PAR) scores was obtained. These culminate in two sets of ranking. The third step seeks validation of results from the head of the partnering organization that has undertaken the projects.

The findings of this study indicate that the 3D integration model is accurate and reliable in measuring the success of project delivery in construction projects of various sizes, locations and durations. While the model uses six key performance indicators (KPIs) to measure delivery success, it is evident that three of these may significantly improve the likelihood of PDS: value, speed and impact. Project managers should focus on these priority aspects of performance to generate better results.

Restrictions inherent to the case study approach are identified for this mixed-method multiple-case study research. There is a limitation on the sample size in this study. Despite the researcher's best efforts, no other firm was willing to share such essential data; therefore, only 40 case studies could be analysed. Nonetheless, the number of case studies met the literature's requirements for adequate units for multiple-case research. This research only looked at Australian construction projects. Thus, the conclusions may not seem applicable to other countries or industries. The authors investigated testing the PDS in the construction sector. It can assist in improving efficiency and resource optimization in this area. Nonetheless, the same technique may be used to analyse and rank the success of non-construction projects.

Despite the research conducted previously on the PDS of construction projects, there is still confusion among researchers and practitioners about what constitutes a successful project delivery. Although several studies have attempted to address this confusion, no consensus on consistent performance metrics or a practical project success model has been formed. More importantly, (1) the ability to measure success across multiple project types, (2) the use of triple bottom line (TBL) to incorporate sustainability in evaluating delivery success and (3) the use of a complexity measurement tool to adjust delivery success scores set the 3D integration model apart from others.

The purpose of this paper is to present a framework of ideation pathways that organically extend the current stock of knowledge to generate new and useful knowledge. Although detailed, granular guidance is available in the strategy literature on all aspects of empirically testing theory, the other key aspect of theory development – theory generation – remains relatively neglected. The framework developed in this paper addresses this gap by proposing pathways for how new theory can be generated.

Grounded in two foundational principles in epistemology, the Genetic Argument and the open-endedness of knowledge, I offer a framework of distinct pathways that systematically lead to the creation of new knowledge.

Existing knowledge can be deepened (through introspection), broadened (through leverage) and rejuvenated (through innovation). These ideation pathways can unlock the vast, hidden potential of current knowledge in strategy.

The novelty and doability of the framework can potentially inspire research on a broad, community-wide basis, engaging PhD students and management faculty, improving knowledge, democratizing scholarship and deepening the societal footprint of strategy research.

Knowledge is open-ended. The more we know, the more we appreciate how much we don’t know. But the lack of clear guidance on rigorous pathways along which new knowledge that advances both theory and practice can be created from prior knowledge has stymied strategy research. The paper’s framework systematically pulls together for the first time the disparate elements of transforming past learning into new knowledge in a coherent epistemological whole.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

This study aims to investigate how social entrepreneurial role models influence social entrepreneurial self-efficacy, social entrepreneurial intent and social entrepreneurial action, with moral obligation as a moderator.

A cross-sectional survey of 261 pupils in the South African province of the Eastern Cape was used in the research study. Structural equation modeling was used to test hypotheses.

The research revealed that having social entrepreneurial role models has a positive impact on both social entrepreneurial self-efficacy and social entrepreneurial intent. In addition, a connection was found between social entrepreneurial intent and entrepreneurial action. The influence of moral obligation was found to be a positive and a significant moderator. Moreover, the association between social entrepreneurial role models and social entrepreneurial intent was mediated by social entrepreneurial self-efficacy.

The findings are not generalizable to nonstudent samples because students constituted the sample for gathering data. Future study therefore requires considering nonstudents to generalize the outcomes. This research should be replicated in other South African provinces and other developing countries for comparative outcomes.

Since social entrepreneurial role models have been practically linked to social entrepreneurship intent and entrepreneurial efficacy, understanding the factors that influence student’s decision to start a social enterprise is critical in South Africa to develop targeted interventions aimed at encouraging young people to start new businesses. Policymakers, society and entrepreneurial education will all benefit from the findings.

This study contributes to bridging the knowledge gap as it investigates how social entrepreneurial role models influence social entrepreneurial self-efficacy, social entrepreneurial intent and social entrepreneurial action, with moral obligation as a moderator. Encouraging social entrepreneurship among South African youth would also help address societal issues. This is a pioneering study in the context of an emerging economy such as South Africa, where social entrepreneurship is so integral.