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This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

The current study uses an advanced machine learning method and aims to investigate whether auditors perceive financial statements that are principles-based as less risky. More specifically, this study aims to explore the association between principles-based accounting standards and audit pricing and between principles-based accounting standards and the likelihood of receiving a going concern opinion.

The study uses an advanced machine-learning method to understand the role of principles-based accounting standards in predicting audit fees and going concern opinion. The study also uses multiple regression models defining audit fees and the probability of receiving going concern opinion. The analyses are complemented by additional tests such as economic significance, firm fixed effects, propensity score matching, entropy balancing, change analysis, yearly regression results and controlling for managerial risk-taking incentives and governance variables.

The paper provides empirical evidence that auditors charge less audit fees to clients whose financial statements are more principles-based. The finding suggests that auditors perceive financial statements that are principles-based less risky. The study also provides evidence that the probability of receiving a going-concern opinion reduces as firms rely more on principles-based standards. The finding further suggests that auditors discount the financial numbers supplied by the managers using rules-based standards. The study also reveals that the degree of reliance by a US firm on principles-based accounting standards has a negative impact on accounting conservatism, the risk of financial statement misstatement, accruals and the difficulty in predicting future earnings. This suggests potential mechanisms through which principles-based accounting standards influence auditors’ risk assessments.

The authors recognize the limitation of this study regarding the sample period. Prior studies compare rules vs principles-based standards by focusing on the differences between US generally accepted accounting principles (GAAP) and international financial reporting standards (IFRS) or pre- and post-IFRS adoption, which raises questions about differences in cross-country settings and institutional environment and other confounding factors such as transition costs. This study addresses these issues by comparing rules vs principles-based standards within the US GAAP setting. However, this limits the sample period to the year 2006 because the measure of the relative extent to which a US firm is reliant upon principles-based standards is available until 2006.

The study has major public policy suggestions as it responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US Securities and Exchange Commission (SEC), to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the International Accounting Standards Board (IASB) Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks such as climate change.

The study has major public policy suggestions because it demonstrates the value of principles-based standards. The study responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US SEC, to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information as business transactions and investor needs continue to evolve globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the IASB Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks like climate change. The study fills the gap in the literature that auditors perceive principles-based financial statements as less risky and further expands the literature by providing empirical evidence that the likelihood of receiving a going concern opinion is increasing in the degree of rules-based standards.

This study aims to investigate the influence of perceived luxuriousness on consumers’ revisit intentions via the mediating effects of positive and negative emotions based on the Stimulus-Organism-Response (SOR) model. In this context, “luxuriousness” specifically refers to the richness of furnishings, including the visual allure of aesthetic design and the surrounding cues.

A quantitative approach using a survey method is employed to analyse the collected 289 data from consumers of bubble tea. Partial least squares structural equation modelling is chosen as the main analytical approach to examine the research model.

The results showed that perceived luxuriousness has a significant positive influence on positive emotion and a significant negative influence on negative emotion. Furthermore, positive emotion positively affects revisit intentions, whereas negative emotion negatively affects revisit intentions. Positive emotion mediates the relationship between perceived luxuriousness and revisit intentions, but negative emotion does not.

In terms of theoretical contributions, this study contributes to the SOR model by exploring the influence of perceived luxuriousness on revisit intentions via the mediating effects of emotions in the bubble tea context, which has not been previously examined by past studies. In terms of managerial implications, this study provides insights into how to leverage the element of luxury to encourage consumers to revisit bubble tea stores.

This study aimed to understand better what makes esports fans engage with streamers' live-streaming of esports gameplay. This study used the Theory of Planned Behavior (TPB) and additionally adopted streamer identification and esports game identification as moderating variables.

Data were collected from streamers' esports content streaming viewers over 18 years of age using an online survey in Amazon M-Turk (N = 307). Based on past esports live-streaming weekly watching hours, which range from 1 to 45 h, the participants were divided into lower (n = 152) and higher (n = 155) frequency groups. PLS-SEM and bootstrapping techniques were used to test the moderated mediation relationships among the constructs.

This study found a negative moderating effect of past watching experience on the relationship between attitudes and behavioral intention, and it positively moderated the path between perceived behavioral control and behavioral intention. Also, it was found statistically significant direct impacts of streamer identification (STI) and esports game identification (EGI) on attitude and subjective norms. While the indirect impact of STI on behavioral intention through attitude was statistically significant, there were no significant indirect impacts of EGI on attitude and behavioral intention through subjective norms.

Theoretically, this study extends the TPB model by exploring the two identifications (i.e. streamers and esports games) as antecedents of the focal TPB factors (i.e. attitudes, subjective norms and perceived behavioral control) and the moderating effect of prior experience based on high/low weekly watching frequencies. Practically, content creators of esports live-streaming and live-streaming platform managers can use the study’s findings to develop strategies to nurture their current and future viewership.

Understanding how to retain users of augmented reality (AR) shopping apps and to motivate them to purchase is vital to the success of AR apps. This study assessed the chain effect of AR attributes on purchase intention and reuse intention through cognitive and affective factors.

The data were collected from Thai users of the IKEA Place app using an online survey. A link to the survey was posted on Thai furniture groups on social media platforms. The 439 responses were analysed using the partial least squares (PLS) approach.

The results revealed that all four AR attributes, namely interactivity, vividness, novelty and spatial presence, significantly influence perceived enjoyment, perceived diagnosticity and perceived value. Brand attitude, as a key driver of purchase intention, is influenced by perceived value. Attitude towards the app significantly affects reuse intention and is affected by affective and cognitive factors.

The findings enable shopping app designers and marketers to successfully promote the brand, retain users and boost sales by effectively incorporating AR.

The study extends the literature on the impacts of AR apps on customer behaviours by including affective factors in addition to cognitive factors to explain why AR attributes influence customer attitudes and behaviours. Furthermore, the study demonstrates the serial causal paths from AR attributes to customer behaviours.

Currently, consumers of financial products and services have become more vulnerable to predatory financial institutions, especially in the aftermath of Covid-19 pandemic. Therefore, financial consumers like the persons with disabilities (PWDs) should be equipped with knowledge and skills to help them to evaluate complex financial products on offer in financial markets, especially in developing countries to avoid being victims of fraudulent lending. The purpose of this study is to establish whether customized financial literacy mediates the relationship between financial consumer protection and financial inclusion of PWDs’ owned MSMEs in rural Uganda post Covid-19 pandemic.

SmartPLS 4.0 was used to construct the measurement and structural equation models to test whether customized financial literacy significantly mediates the relationship between financial consumer protection and financial inclusion of PWDs’ owned MSMEs in rural Uganda post Covid-19 pandemic.

The results revealed a partial mediating effect of customized financial literacy in the relationship between financial consumer protection and financial inclusion of PWDs’ owned MSMEs in rural Uganda post Covid-19 pandemic. Conducting customized financial literacy increases financial consumer protection by 12 percentage points to promote financial inclusion of PWDs’ owned MSMEs in rural Uganda post Covid-19 pandemic.

This study focused only on customized financial literacy and financial consumer protection to promote universal financial inclusion of PWDs’ owned MSMEs post Covid-19 pandemic. Future studies may use data collected from other vulnerable groups amongst the unbanked population in developing countries, Uganda inclusive. In addition, this study also collected only quantitative data from the selected population. Further studies can be conducted using key informant interviews and focused group discussion to get the perceptions of the PWDs on being protected from exploitation by unscrupulous financial institutions.

The findings from this study can help policymakers in developing countries like Uganda to revise the existing consumer protection law to include strong clauses on protection of people with special needs like the PWDs. The law must ensure that they are not exploited by financial institutions because of their conditions. The law ought to make sure that the PWDs are educated about their rights in the financial market place and all information on financial products offered by financial institutions should be simplified and interpreted to them before they make consumption decisions.

To the best of the authors’ knowledge, the present study is amongst the first few studies to provide a meticulous and unique discourse on the ever increasing role of financial literacy combined with consumer protection to reduce consumption risks within the financial markets, especially in developing countries in the aftermath of global pandemic shocks. This study uses the social learning theory, theory of reasoned action and theory of planned behaviour to elucidate how customized financial literacy can enhance consumer protection to increase financial inclusion of groups with special needs like the PWDs who have become more susceptible to exploitation by unscrupulous financial institutions in under-developed financial markets, especially in post Covid-19 pandemic.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.