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The effects of integrated reporting quality (IRQ) have been debated in increasing empirical studies. Several IRQ measures, different theoretical approaches and multiple contexts have been adopted and investigated, leading to mixed results. By using the meta-analytic technique, this study aims to contribute to the accounting literature, reconciling the conflicting results on the effects of IRQ and providing objective conclusions to complement narrative literature reviews.

A sample of 45 empirical papers from 2013 to 2022, with 653 effect sizes, was used to assess the effects associated with IRQ. The papers were clustered into five groups (market reaction, financial performance, cost of capital, financial analysts’ properties and managerial decisions) based on the different consequences of IRQ investigated in the primary studies. A random-effects meta-regression model was used to explore all sources of heterogeneity together.

The meta-regression results confirm that IRQ positively influences firms’ market valuation and financial performance and hampers opportunistic managerial behaviour by improving corporate transparency, mitigating information asymmetry and encouraging accountability. Moreover, differences in the study characteristics affect the strength of the relationship object of interest.

Through meta-analysis, this study provides a broader overview of the effects of IRQ by enhancing the generalisability of the findings. The results also pave the way for additional evidence on the outcome variables affected by the quality of integrated disclosure.

Organizations increasingly use knowledge arbitrage to stimulate innovation and achieve competitive advantage. However, in knowledge management its use in startups is yet unexplored. This study aims to examine the utilization of knowledge arbitrage by startups, specifically during COVID-19.

This study employed an open-ended essay methodology to explore the drivers and barriers that startups face in utilizing knowledge arbitrage. We collected data from 40 participants to understand the role of knowledge arbitrage in startups’ knowledge management practices.

This study’s findings highlight the significance of knowledge arbitrage for startups. The benefits identified include organizational benefits such as building networks, innovating new products and achieving competitive advantage and financial benefits such as cost reduction and sales growth. The study also identifies several technological and organizational drivers and barriers that startups confront during knowledge arbitrage.

This study contributes to the existing literature on knowledge management by extending our understanding of knowledge arbitrage’s role in startups. Additionally, it sheds light on the importance of knowledge arbitrage for startups and the challenges they face, particularly in a disrupted environment reared by COVID-19. The study provides insights for the scholars and practitioners interested in effective knowledge management in startups.

We develop a credit-risk model to study the informational role of investment in an economy susceptible to large liquidity shocks. Firms' investment decisions carry information about their asset quality, thereby mitigating informational frictions when firms enter bankruptcy. An increase in aggregate investment can reduce the informational value of investment, depressing firms' recovery values. Therefore, policies boosting investment can decrease debt and firm values by reducing the informational value of investment. The presence of debt overhang may enhance firm value by making firms' investment decisions more informative. We present suggestive empirical evidence consistent with model predictions on the relation between firms' investments and recovery rates.

In modern societies, consumer well-being is dependent on choices regarding complex services, such as investments, health care, insurance and lending. However, evaluating costs of such services is often difficult for consumers due to a combination of limited cognitive resources and complexity of the service. The purpose of this study is to empirically examine to what extent three specific consequences of complexity influence consumer tendencies to make mistakes when evaluating the costs (or price) of complex services.

Three studies were conducted (survey: n = 153, experiment: n = 332 and conjoint analysis: n = 225), all focusing on how consumers evaluate costs in the complex mutual fund setting.

The authors find that consumers struggle with estimating and using cost information in decision-making in the complex services setting. Consumers of complex services frequently underestimate the costs over the long-term, may see costs as a signal of service quality and are susceptible to influence from presentation formats when evaluating costs.

The study investigates mutual funds, which is one example of a complex service. In order to get a full picture of how consumers deal with costs in complex setting, future research needs to expand this focus to other types of complex services.

The results have implications for both marketers of complex services and policymakers. For marketers, this paper highlights that competing with a low-cost strategy may be difficult in the complex services setting as consumers may lack the ability to actually evaluate what they pay over the long term. For policymakers, increased simplification of prices may be an attractive option. However, it is important that this simplification is done in a way that increases the possibility to compare prices.

As complexity influences several aspects of decision-making, an understanding of how consumers evaluate costs in complex settings is dependent on taking a multidimensional research approach. This paper makes a novel contribution to the literature on pricing by showing that consumers struggle with multiple aspects when evaluating costs in complex contexts. Understanding these effects is important to policy, as well as to research on the cognitive value of simplicity that is currently gaining traction in marketing research.

One of the greatest challenges faced by the 1994 post-apartheid government in South Africa is the housing problem that has persisted for almost 30 years into democracy. Innovation in research and practice is required to address this problem. This paper aims to discuss the aforementioned objective.

This paper presents an argument for housing knowledge management as one part of a much larger system of housing provision and critically compares information variations on one hypothetical, low-cost housing unit adapted for varying climatic regions. It aims to enquire if there is an overlap in information.

The findings do confirm a noteworthy overlap in the information of the varying units. Therefore, knowledge management of the information would prove effective and may contribute in part to housing provision.

The study is limited to assessing the information changes made to the contract documentation of the housing unit.

The paper argues that knowledge management of this overlapping information could impact housing provision by providing knowledge power to those affected by the housing problem.

The findings are a unique perspective presented through a knowledge management lens. In addition, the said knowledge management lens provides a platform to raise additional questions. When seeking answers to these questions, it is expected that research sub-themes would be identified focussing further research studies towards finding answers.

This paper aims to examine the financial outcome of reverse logistics among firms in a developing country. The authors draw on the organizational information processing theory to propose that analytics capability moderates the relationship between reverse logistics and financial performance.

The authors collected firm-level survey data from 200 manufacturing firms in Ghana, a developing country in sub-Saharan Africa. Partial least squares structural equations modeling is used to examine the proposed relationships, and the moderating effects are further probed using Hayes PROCESS.

The empirical results show that reverse logistics is negatively related to financial performance. However, analytics capability attenuates this negative relationship, such that firms with high analytics capability obtain a positive relationship between reverse logistics and financial performance.

Firms in developing countries should combine their reverse logistics strategies with developing analytics capabilities that help minimize uncertainties and increase the efficient collection and use of information to reduce the cost of reverse logistics.

This paper examines how reverse logistics relates to financial performance in low-resource contexts. Beyond the novelty of the context, it explores the information processing needs of reverse logistics systems and provides empirical data to support analytics capability. This has yet to be considered in prior studies.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

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 analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.

Drawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.

Compared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.

This research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

This study aims to explore natural resources from a public sector accounting perspective, focusing on their definitions, classifications, recognition criteria and disclosure requirements provided by different standard-setters and regulators at both international and national levels.

By reviewing accounting frameworks for natural resources, this study extrapolates accounting dilemmas around the debate on natural resource accounting, using the dialogic accounting perspective as a theoretical framework.

Natural resources cannot be defined as a single category. Various categories have different characteristics, requiring different standards to recognize multiple orientations. This avoids monetary reductionism. Furthermore, uncertainty, both in existence and measurement, may disqualify some of these resources from being considered assets. Perhaps, concentrating on the flow of services derived from natural resources is better than focusing on their valuation. This may lead to a split-asset approach (flows and underlying assets) for certain resources. This study’s findings indicate that public-sector entities should consider preparing a separate non-financial report regarding the management of natural resources with the objective of maintaining inter-generational equity.

This study contributes to the debate on natural resources from an accounting and reporting perspective, highlighting the importance of holding public-sector entities accountable for the use of natural resources.