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The study seeks to identify and document definitional challenges that hamper the delineation of the scope of real estate as a discipline and as an industry. Through literature review the article distils the perception of body of knowledge (BOK) of real estate within the academia. Two main issues are flagged up: the problem of undefined BOK and the collegiate dilemma. Later the study looks at the standard economic classification documents to capture the occupational domains of real estate professionals or real estate activities. These steps are necessary to help define an alternative academic, practical and social meaning of real estate that is sufficient and precise.

The study uses literature review and, as primary method, qualitative document analysis (QDA). The study has made a special appeal for the application of qualitative strategy in real estate research other than following the methodological orthodoxy of quantitative causal research designs. Further, it has argued for the recognition of QDA as a legitimate research method in the context of real estate studies. Consequently, the study performed QDA procedures on international economic classification standards.

From literature review and QDA, the study identified five definitional problems in the meanings or understandings of real estate: undefined body of knowledge, collegiate dilemma, inadequate classification of real estate occupations, inadequate industry classification and inadequate economic sector positioning. These are aspects that lead to misconceptions of the true boundary of knowledge in society and in the academia. The paper offers clarity and insights for the redrawing of these boundaries to give real estate its rightful place in the academia and in the real world.

The article follows up on the academic and social misconceptions on the BOK of real estate as a discipline and an economic activity domain to identify the contribution of real estate to the welfare of mankind. Ontology or the organization of academic or social knowledge is used to map out or catalogue real estate against competing domains and to show that the role of real estate is grossly understated and misunderstood. From the findings, the study makes recommendations to university curriculum developers, and international organizations like ILO, and UN-DESA to revise their conceptions of real estate to give the discipline its rightful position in society.

This paper empirically explored the influence of external and internal factors on technological and non-technological innovation of 5747 Indian firms. The study also explored novel insights about manufacturing firms by segregating them into high-technology and low-technology industries.

The study employed hierarchical regression analysis to analyse a cross-sectional dataset gathered from the World Bank enterprise survey. The firms are segregated into high-technology and low-technology industries based on the technology-intensity classification of the manufacturing industry given by the Organisation for Economic Co-operation and Development.

The main results highlight that technological and non-technological innovation was primarily driven by internal resources and capabilities rather than external factors. The authors found the highest effect of research and development spending on both forms of innovation. In both high-tech and low-tech industries, technology transfer is positively associated with technological innovation and negatively associated with non-technological innovation. Furthermore, external business support has substantially influenced non-technological innovation in low-tech industries.

This study used two-step hierarchical regression to explore the influence of external and internal factors on technological and non-technological innovation separately. Exploring determinants of innovation in high-technology and low-technology industries also brings the distinct prerequisites of enhancing innovation to the attention of policymakers and industry experts.

Drawing on the resource dependence theory (RDT), this paper investigates ownership composition, export intensity, and industry class as moderating factors to investigate the role of imported raw materials in performance of inward foreign direct investment (IFDI) in Ethiopia.

The hypotheses were tested using secondary data obtained from the 2016 Central Statistical Agency (CSA) on Large- and Medium-Scale Manufacturing and Electricity Industries Survey. The data included basic quantitative information on the country's manufacturing industry. The data items for the 2016 manufacturing and electricity industries surveyed are the numbers of proprietors or establishments involved in various sectors. The report did not record small firms that employed fewer than 10 people and did not use power-driven machinery. Two-Stage least squares (2SLS) regression analysis was performed to test the proposed hypotheses.

The results of this study indicate that three moderators (ownership composition, export intensity, and industry classification) interact with the hypothetical relationships between imported raw materials and performance. These findings enrich the knowledge of IFDI firms' operations in Ethiopia and in other least-developed countries (LDCs). The findings could provide information for IFDI firms that are looking to invest in LDCs.

Like all social science research, this study has some limitations. First, the research was conducted with the data found in the Report on Large- and Medium-Scale Manufacturing and Electricity Industries Survey In 2016. This was the first year of the second five-year Growth and Transformation Plan (GTP II), a national development plan for the 2016–2020 period. Continual research on IFDI in Ethiopia in the following years will be needed to get a full picture of the effects of the determinants on IFDIs.

To IFDI investors, the result of this thesis demonstrates several alternatives to overcoming hurdles in manufacturing operation. The results find that J.V. firms make better use of imported raw materials than W.O. subsidiaries in order to achieve better performance. Concerning the choice between focusing on export or domestic markets, the study suggests that domestic market—oriented companies require less imported raw materials to achieve better performance. Concerning the comparative advantage on different industries, this study found the performance of firms in Industry 12 depended on imported raw materials. These findings highlight the challenges and opportunities for potential foreign investors. Ownership composition, market factors, and industry factors should be well considered in making investment decisions.

This is one of few studies on IFDI in Ethiopia, the most populous LDC. Ownership composition, export intensity, and industry class are used as moderating variables to investigate the difference between imported raw materials and the level of expatriate deployment to IFDI performance. For IFDI investors, the results of this study demonstrate several alternatives to overcoming hurdles in manufacturing operation.

In this paper, the knowledge-based view of the firm is applied to theoretically elucidate and empirically examine the influence of target industry growth on premium payments in cross-border acquisitions. This study aims to extend internally driven Mergers and Acquisitions research efforts of acquirers to industry contingencies of targets by analyzing cross-border acquirers located within the group of seven (G7) countries.

This study’s investigation covers 209 cross-border acquisitions from 2012 to 2019 undertaken by firms located within the G7 countries. This paper used generalized estimation equations method to test the hypotheses applying Gaussian distribution for the dependent variable, an identity link function, exchangeable correlation structure and robust standard errors.

This study’s results reveal that target industry growth determines premiums. Furthermore, this study shows that acquirer industry growth, as well as acquirer slack, moderates this relationship. This study is built on the notion that industry contingencies influence premiums even when target firms are based in foreign countries.

To the best of the authors’ knowledge, unlike other studies, this is the first to explicitly focus on premiums for multiple cross-border acquisitions by hand selecting nine years of industry sale figures for 53 industries separately in each of the G7 nation countries (leading to 371 separate downloads of data samples). This study contends that industry growth of cross-border targets matters for premium payments.

This paper aims to examine the relationship between sustainability reporting and firm performance in New Zealand, encompassing the materiality concept of sustainability reporting based on the newly available sustainability reporting standards of the Sustainability Accounting Standards Board (SASB). This set of disclosure items published in 2018 is likely to impact on investors’ decision-making and firm performance, as stipulated by the SASB.

Using a sample of 84 New Zealand companies during the period 2017–2019 and an ordinary least squares statistical approach, this research examines whether firms disclosing sustainability reporting and financially material sustainability information have better performance than the ones non-disclosing.

Consistent with the legitimacy and stakeholder theories, a positive relationship between sustainability reporting and performance is observed. This positive association is stronger when the sustainability disclosure is financially material information as defined by the SASB.

The outcome of this study provides evidence of the financial incentives for firms to initiate sustainability reporting, especially including financially material sustainability information as guided by the SASB. It also supports the rationale of the SASB for developing new standards that can be globally applicable, influencing investors’ decisions and firm’s financial performance. The results also have implications for the management of New Zealand firms in considering the disclosure of material sustainability information which is linked to firm performance.

With the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.

This article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.

This article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.

Firstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.

The coronavirus disease 2019 (COVID-19) outbreak in the first quarter of 2020 has caused a severe decline in stock markets worldwide. While prior studies in developed markets found that workplace closure can negatively impact the capital market (e.g. Ozili and Arun, 2020), lesser is known about how it impacts emerging capital markets, which may have different characteristics and behaviour (Harjoto et al., 2021). Hence, this study seeks to uncover stock performance around workplace closure dates of firms incorporated in ASEAN countries and investigates the role of accounting fundamentals in mitigating workplace closure policy's effects on stock performances.

Using an event study methodology, the authors measure the cumulative abnormal returns (CARs) around workplace closure dates. The authors then use cross-sectional analysis to analyse whether the accounting fundamentals, specifically profitability, cash flow, and leverage, are associated with the CAR. This cross-sectional study involves 1,720 firms that are incorporated in the ASEAN countries.

This analysis indicates that, on average, ASEAN capital markets react negatively to workplace closure policies. The authors then find that the CARs around workplace closure dates are positively associated with the current ratios and are negatively associated with long-term debt ratios. This study’s results thus indicate that firms with a higher liquidity and a higher solvency experience a less adverse impact of the COVID-19 pandemic than other firms. The authors also find that the associations are more robust for (1) firms in industries more affected by COVID-19 and (2) firms located in countries with more severe cases. Additionally, contrary to this study’s expectation, the authors do not find meaningful associations between CARs around workplace closure dates and firms' cash flow from operation and profit respectively. This study’s results suggest that investors view prior performances related to firms' ability to generate operating cash flow and profit as less relevant to measure firm performance around the workplace closure event.

This study’s results contribute to studies examining fundamental accounting roles during the COVID-19 era, specifically in emerging economies. The findings are critical for investors in understanding the company fundamentals associated with stock price performance in emerging markets during the recent health-related crisis.

Most studies analysing cross-sectional differences in stock returns during the COVID-19 era focus on industry-level differences and use observations from developed markets (Sinagl, 2020; Ramelli and Wagner, 2020). Studies using firm-level analysis in emerging markets are still limited. The authors expand prior studies by using firm-level analysis that spans six countries in ASEAN.

This study considers transitive service triads, which consist of three dyads formed by three actors: supplier, logistics service provider and customer, who remain directly linked by one or more of the upstream and downstream flows of products, information and finances. This paper aims to explore the link between information governance, decentralized information technologies and supply chain self-organization, and their resulting impact on network performance in the transitive service triads.

Drawing upon the tenets of the theory of complex adaptive systems and supply chain practice view, this paper involves an empirical investigation that uses survey data gathered from transitive service triads in the European countries. The study uses partial least squares structural equation modeling to estimate the formative-reflective hierarchical component model and test the research hypotheses.

Information governance defines how supply chain information flows are controlled, accessed and used by a focal organization and its business partners. As empirically evidenced in this study, it can be depicted as a latent construct consisting of three distinct dimensions of information custody, information ownership and right to data access. Likewise, the study also indicates that supply chain self-organization, as a second-order construct, consists of three interactive self-organization actions undertaken by specific firms participating in the triadic arrangement. Supply chain self-organization is thus produced by firms that are reciprocally interrelated and interacting, having effects on one another. Furthermore, the study also highlights that information governance creates an environment for applying decentralized information technologies, which then positively affects supply chain self-organization. Finally, the research also empirically operationalizes the construct of network performance within the transitive service triads.

Although the results provide several major contributions to theory and implications for practitioners, the study still demonstrates some methodological constraints. Specifically, although the study uses a relatively large research sample of 350 transitive service triads, it still focuses only on a selected group of industries and is limited to investigating solely a particular type of service triads.

Given the increasing interest in investigating triads, this study examines how information governance and decentralized information technologies support supply chain self-organization to yield network performance in transitive service triads.

This study aims to investigate the external effect of the economic growth target pressure of local governments on establishment-level SO₂ emissions.

Based on manually collected panel data of 74,058 China's industrial establishments and more than 330 thousand observations from CIED and ESR, the authors use a firm-fixed effect model, instrumental variables estimation and heterogeneity tests to identify the environmental externality of economic growth target pressure.

The establishments in cities that meet or slightly exceed the economic growth target experience greater negative externality measured by SO₂ emission intensity. This external effect is more pronounced in regions: with a strict and overweighted target setting; with stronger officials' promotion incentives; with a low degree of marketization; and in firms with great economic importance. The authors identify the underlying mechanisms of dependence on dirty industry and the relaxation of environmental enforcement. And the environmental protection constraints in 2007 mitigate the negative externality.

The paper sheds light on to what extent economic growth target pressure has a negative externality of pollution in China and how this pressure may conflict with environmental protection.

This paper complements prior research on the economic effects of economic growth targets, expands the knowledge on the determinants of establishment-level pollution emission from the perspective of target pressure and provides insight into the environmental externality that results from political factors.

This study aims to examine earnings management around initial public offerings (IPOs) in India. It also explores the influence of issue characteristics on earnings management around the IPOs.

A sample of 511 IPOs that came during April 2003-March 2019 is studied for calculating earnings management for pre-issue, issue and post-issue years. Using Cross-Sectional Modified Jones Model, the paper presents earnings management on the basis of three proxies i.e. discretionary accruals, discretionary current accruals and discretionary long-term accruals. The influence of issue characteristics on earnings management practised around the IPOs is also observed through correlation and multiple regression analysis.

The paper finds that earnings management is abnormally high during the issue year compared with pre-issue and post-issue years. It also unveils that profitability, premium, age, and size of the issuer significantly determine the level of pre-issue and issue year earnings management practised by Indian IPO issuers.

The findings are useful to stakeholders (potential investors, analysts and regulators) to observe, assess and understand the quality of financial numbers that are based on fallacious disclosure of accounting figures. It provides insight into the possibilities of managed earnings around the issue that could influence investors’ decision-making. Further, the study reflects the efficacy of Indian regulatory norms for IPOs.

To the authors’ knowledge, it is the only Indian study that had used an extensive data set of about two decades to calculate earnings management during pre-issue, issue and post-issue years. The uniqueness of the study further lies in three proxies of earnings management representing short-term and long-term accruals. Moreover, it is the first study to observe the influence of IPO issue characteristics on earnings management.