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Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

One popular method to assess discriminant validity in structural equation modeling is the heterotrait-monotrait ratio of correlations (HTMT). However, the HTMT assumes tau-equivalent measurement models, which are unlikely to hold for most empirical studies. To relax this assumption, the authors modify the original HTMT and introduce a new consistent measure for congeneric measurement models: the HTMT2.

The HTMT2 is designed in analogy to the HTMT but relies on the geometric mean instead of the arithmetic mean. A Monte Carlo simulation compares the performance of the HTMT and the HTMT2. In the simulation, several design factors are varied such as loading patterns, sample sizes and inter-construct correlations in order to compare the estimation bias of the two criteria.

The HTMT2 provides less biased estimations of the correlations among the latent variables compared to the HTMT, in particular if indicators loading patterns are heterogeneous. Consequently, the HTMT2 should be preferred over the HTMT to assess discriminant validity in case of congeneric measurement models.

However, the HTMT2 can only be determined if all correlations between involved observable variables are positive.

This paper introduces the HTMT2 as an improved version of the traditional HTMT. Compared to other approaches assessing discriminant validity, the HTMT2 provides two advantages: (1) the ease of its computation, since HTMT2 is only based on the indicator correlations, and (2) the relaxed assumption of tau-equivalence. The authors highly recommend the HTMT2 criterion over the traditional HTMT for assessing discriminant validity in empirical studies.

This study examines the moderating effects of low and high levels of voluntary disclosures (VDs) between corporate governance and information asymmetry (IA).

The study used PROCESS macro to construct bootstrap confidence intervals at the 95% level to estimate the model, and “simple slope analysis” to visualize the model.

The better corporate governance provides a monitoring mechanism that disseminates private information and reduces IA. The effect of corporate governance on IA is contingent on the levels of VDs within a firm, and this relationship is strengthened when the level of VDs within a firm is high, and results remain consistent when levels of sub-indices are high. Additional analysis reveals that effective boards and audit committees reduce IA. Increased inside, an associated company, family and foreign ownership exacerbate IA, whereas institutional owners act as effective monitors to overcome informational disadvantages.

The findings provide implications for policymakers to promote corporate governance and more relevant reporting practices as effective mechanisms for protecting shareholders' rights and attenuating IA in capital markets.

The study is valuable to understand the strength of the relationship between corporate governance and information asymmetries based on the moderating role of different VD levels.

This paper aims to test three hypotheses in city growth literature documenting the poverty reduction observed in Brazil and exploring a rich spatial dataset for 5,564 Brazilian cities observed between 1991 and 2010. The large sample and the author's improved econometric methods allows one to better understand and measure how important income growth is for poverty reduction, the patterns of agglomeration and population growth in all Brazilian cities.

The author identifies literature gaps and use a sizeable spatial dataset for 5,564 Brazilian cities observed in 1991, 2000 and 2010 applying instrumental variables methods. The bias-corrected accelerated bootstrap percentile interval supports the author's point estimates.

This manuscript finds that Brazilian data for cities does not support Gibrat's law, raising the scope for urban planning and associated policies. Second, economic growth on a sustainable basis is still a vital source of poverty reduction (The author estimates the poverty elasticity at four percentage points). Lastly, agglomeration effects positively affect the city's productivity, while negative externalities underlie the city's development patterns.

Data for cities in Brazil possess unique characteristics such as spatial autocorrelation and endogeneity. Applying proper methods to find more reliable answers to the above three questions is a desirable procedure that must be encouraged. As the author points out in the manuscript, dealing with endogenous regressors in regional economics is still a developing matter that regional scientists could more generally apply to many regional issues.

While empirical studies establish the importance of procurement planning in achieving value for money (VfM) in procurement, there is scant evidence demonstrating a link between procurement planning and procurement regulatory compliance, and thus VfM. As a result, this study examined how procurement regulatory compliance can be applied when procurement practitioners in Tanzania seek to maximize VfM through procurement planning.

A cross-sectional research design was adopted from which data were collected once through a structured questionnaire. The structural equation modeling (SEM) and Hayes' PROCESS macro test for mediation analysis were used to analyze the collected data.

Procurement planning has a significant and positive relationship with procurement regulatory compliance (ß = 0.491, p < 0.001). Procurement regulatory compliance has a significant and positive relationship with VfM in procurement (ß = 0.586, p < 0.001). Results also show that procurement planning is a significant positive predictor of VfM (ß = 0.257, p = 0.005). Furthermore, the bootstrapping confidence intervals revealed that procurement regulatory compliance significantly mediates the relationship between procurement planning and VfM in procurement.

Although the study was able to accomplish its overall objective, it is limited in terms of the geographical setting under which the study was conducted. Hence, the generalization of research results should be made with caution as each country has specific public procurement laws and regulations governing the conduct of procurement activities in the public sector.

The study contributes to the growing debate on achieving VfM in procurement activities. The study adds to the literature on public procurement by establishing the mediation effect of procurement regulatory compliance on the quest toward achieving VfM in public procurement.

As technology in tourism and hospitality (TTH) develops technical artifacts according to visitors’ demands, it must deal with both behavioral and design constructs in the context of structural equation modeling (SEM). While behavioral constructs are typically modeled as common factors, the study at hand introduces the composite into TTH to model artifacts. To deal with both kinds of constructs, this paper aims to exploit partial least squares path modeling (PLS-PM) as a confirmatory approach to estimate models containing common factors and composites.

The study at hand presents PLS-PM in its current form, i.e. as a full-fledged approach for confirmatory purposes. By introducing the composite to model artifacts, TTH scholars can use PLS-PM to answer research questions of the type “Is artifact xyz useful?”, contributing to a further understanding of TTH. To demonstrate the composite model, an empirical example is used.

PLS-PM is a promising approach when the model contains both common factors and composites. By applying the test for overall model fit, empirical evidence can be obtained for latent variables and artifacts. In doing so, researchers can statistically test whether a developed artifact is useful.

To the best of the authors’ knowledge, this is the first study to discuss the practical application of composite and common factor models in TTH research. Besides introducing the composite to model artifacts, the study at hand also guides scholars in the assessment of PLS-PM results.

因为旅游酒店科技（TTH）根据游客需求而定制科技产品, TTH必须在结构方程模型（SEM）下结合游客行为和设计等变量。一般行为变量在模型中是常见因子, 本研究将这些变量编入TTH结构成为模块。本研究采用PLS-PM方法来预估含有隐性变量和模块的模型。.

本研究设计PLS-PM模式, 即确定性全变量方法。TTH学者们通过引进结构形成模型模块, 使用PLS-PM研究方法, 以回答研究问题“模块xyz有用吗？”, 因此对TTH进一步理解。为了展示复合模型, 本论文采用实际验证。.

PLS-PM在面对模块内存在常见因子和复合模块的结构时是有力方法。实际验证结果通过整体最佳模型参数, 得到隐性变量和模块。为此, 研究者们能够在统计方法上测量是否开发的模型模块是否有用。.

据作者所知, 本论文是首个研究在TTH领域上应用模块和常见因子模型。本研究引进显性变量在模型模块中, 以指导学者评估PLS-PM结果报告。.

Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues.

This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations.

PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible.

This paper provides updated guidelines of how to use PLS and how to report and interpret its results.

The combination of strategic orientation and digitalization for sustainable competitive advantage among small businesses is still not given much attention in the literature. Therefore, this study aims to understand the influence of strategic orientation on sustainable competitive advantage while mediating the relationship with digitalization.

This study used a cross-sectional design. This design helped collect data from 234 small businesses in Arusha city, Tanzania. Since the study used latent variables, structural equation modeling (SEM) was used to analyze relationships and conduct confirmatory factor analysis. Through bootstrapping confidence intervals, Hayes's Process was also used to test how digitalization mediates the relationship between strategic orientations and sustainable competitive advantage.

The strategic orientation attributes that include market orientation, entrepreneurial orientation and learning orientation were positively and significantly related to digitalization. Furthermore, the results on digitalization and sustainable competitive advantage show a significant positive relationship. Finally, digitalization was analyzed to mediate the relationship between strategic orientation, market orientation, entrepreneurial orientation, learning orientation and sustainable competitive advantage. Hence, all hypotheses were supported.

This study adopted a cross-sectional design that helped to capture the quantitative information. In addition, the current study is limited to Tanzania's small businesses; thus, the findings cannot assure generalization of the conclusion to other countries because of the differences in social, cultural and technology across countries.

This study integrates the concepts of strategic orientation from the strategic management discipline and digitalization from a technology perspective. As a result, the study adds new knowledge about combining two aspects and determining whether they add value in terms of providing a sustainable competitive advantage. This knowledge comes from digitalization, which acts as a mediator between strategic orientation dimensions and a sustainable competitive advantage.

The authors investigate the role of people-related Total Quality Management (TQM) practices, specifically metaperceptions, in hearing care students' vocational decision-making. In Italy, audiologists are health professionals and must hold a degree in hearing care. They operate according to clinical principles but must also develop marketing and commercial skills. While employers take these aspects for granted, the expectations of hearing care students often differ from reality. Thus, the authors aim to investigate the vocational expectations of hearing care students.

A survey was distributed to 600 hearing care students. Multiple regression analysis with bootstrapped confidence intervals was employed to test the hypotheses.

Students who perceived audiology as their calling were more interested in the clinical aspects than the marketing and commercial aspects of audiology. Moreover, those desiring a meaningful career path in audiology were more interested in becoming a store owner or franchisee.

Universities and recruiters should consider the influence of relevant others' metaperceptions on students' self-perceptions of their aptitudes for different careers. Universities should assist students to identify aptitudes that are relevant to career-related decision-making. In this context, people-related TQM can help students avoid incorrect aspirations and expectations.

This study is the first to investigate the role of metaperceptions from a people-related TQM perspective. Metaperceptions play a crucial role in determining the correct course of study as well as job satisfaction and expectations.

(1) A concern often expressed in relation to cryptocurrencies is the environmental impact associated with increasing energy consumption and mining pollution. Controversy remains regarding how environmental attention and public concerns adversely affect cryptocurrency prices. Therefore, the paper aims to introduce the index of cryptocurrency environmental attention (ICEA), which aims to capture the relative extent of media discussions surrounding the environmental impact of cryptocurrencies. (2) The impacts of cryptocurrency environmental attention on long-term macro-financial markets and economic development remain part of undeveloped research fields. Based on these factors, the paper will further examine the effects of the ICEA on financial markets or economic developments.

(1) The paper introduces a new index to capture cryptocurrency environmental attention in terms of the cryptocurrency response to major related events through gathering a large amount of news stories around cryptocurrency environmental concerns – i.e. >778.2 million news items from the LexisNexis News & Business database, which can be considered as Big Data – and analysing that rich dataset using variety of quantitative techniques. (2) The vector error correction model (VECM) and structural VECM (SVECM) [impulse response function (IRF), forecast error variance decomposition (FEVD) and historical decomposition (HD)] are useful for characterising the dynamic relationships between ICEA and aggregate economic activities.

(1) The paper has developed a new measure of attention to sustainability concerns of cryptocurrency markets' growth, ICEA. (2) ICEA has a significantly positive relationship with the UCRY indices, volatility index (VIX), Brent crude oil (BCO) and Bitcoin. (3) ICEA has a significantly negative relationship with the global economic policy uncertainty (GlobalEPU) and global temperature uncertainty (GTU). Moreover, ICEA has a significantly positive relationship with the industrial production (IP) in the short term, whilst having a significantly negative relationship in the long term. (4) The HD of the ICEA displays higher linkages between environmental attention, Bitcoin and UCRY indices around key events that significantly change the prices of digital assets.

The ICEA is significant in the analysis of whether cryptocurrency markets are sustainable regarding energy consumption requirements and negative contributions to climate change. Understanding of the broader impacts of cryptocurrency environmental concerns on cryptocurrency market volatility, uncertainty and environmental sustainability should be considered and developed. Moreover, the paper aims to point out future research and policy legislation directions. Notably, the paper poses the question of how cryptocurrency can be made more sustainable and environmentally friendly and how governments' cryptocurrency policies can address the cryptocurrency markets.

(1) The paper develops a cryptocurrency environmental attention index based on news coverage that captures the extent to which environmental sustainability concerns are discussed in conjunction with cryptocurrencies. (2) The paper empirically investigates the impacts of cryptocurrency environmental attention on other financial or economic variables [cryptocurrency uncertainty (UCRY) indices, Bitcoin, VIX, GlobalEPU, BCO, GTU index and the Organisation for Economic Co-operation and Development IP index]. (3) The paper provides insights into making the most effective use of online databases in the development of new indices for financial research.

Whilst blockchain technology has a number of useful implications and has great potential to transform several industries, issues of high-energy consumption and CO2 pollution regarding cryptocurrency have become some of the main areas of criticism, raising questions about the sustainability of cryptocurrencies. These results are essential for both policy-makers and for academics, since the results highlight an urgent need for research addressing the key issues, such as the growth of carbon produced in the creation of this new digital currency. The results also are important for investors concerned with the ethical implications and environmental impacts of their investment choices.

(1) The paper provides an efficient new proxy for cryptocurrency and robust empirical evidence for future research concerning the impact of environmental issues on cryptocurrency markets. (2) The study successfully links cryptocurrency environmental attention to the financial markets, economic developments and other volatility and uncertainty measures, which has certain novel implications for the cryptocurrency literature. (3) The empirical findings of the paper offer useful and up-to-date insights for investors, guiding policy-makers, regulators and media, enabling the ICEA to evolve into a barometer in the cryptocurrency era and play a role in, for example, environmental policy development and investment portfolio optimisation.