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This paper aims to empirically identify crucial international logistics policy enablers and to examine their impacts on logistics performance using survey data collected from 169 responding firms in Taiwan and 109 responding firms in the UK including logistics companies, freight forwarders, shipping companies, agencies and airline companies.

A multiple regression analysis is used as a method to empirically validate the research model.

Results indicate the five most important logistics policy enablers according to Taiwanese logistics firms are information technology system, inland transport linkage, simplifying the customs clearance procedures, ports and maritime transport and having a policy to ensure efficient service operation and multiplicity of services. In contrast, for the UK logistics firms, the five most important logistics policy enablers are telecommunications, information technology system, avoidance of unnecessary regulation, inland transport linkage and ports and maritime transport. Results also indicate that logistics policy dimensions in terms of regulation, integration, infrastructure and logistics education have a positive influence on firms’ logistics service quality and efficiency.

Theoretical and policy implications from the research findings on logistics policy between these two countries are discussed in this paper.

This study aims to analyze the determinants of ratings of corporate bonds and sukuk issued by firms listed on the Indonesia Stock Exchange (IDX) for the 2013–2019 period.

This study uses a quantitative approach by testing hypotheses and using logistic regression. Ordinal logistic endogenous (or dependent) variables (Y) in ordinal logistics use data in the form of levels (ordinal scale). Independent (or exogenous) variables (X), include financial and non-financial factors for dependent (or endogenous) variables (Y), namely, of corporate bonds and sukuk ratings. There are two approaches to the study they are Logit and Gompit (Negative Log-Log. The population of the study is Indonesian companies listed on the IDX that issued bonds and sukuk for the 2013–2019 periods. The sampling technique is purposive. In total, 16 corporate companies adhering to the above criteria and issuing bonds and sukuk were chosen. In total, 270 types of bonds and 280 types of sukuk were selected as samples.

The results of the Logit and Gompit regression show that leverage ratio, firm size, security structure and maturity date are important determinants of corporate bond ratings while profitability and liquidity ratios appear to have no influence on the rating. In the case of sukuk, profitability, liquidity and maturity date play important roles in influencing the corporate sukuk rating. However, there is no evidence to suggest that leverage ratio, company size and security structure may affect sukuk ratings.

For both sukuk and bond issuers, it is necessary to pay attention to the factors that may affect the ratings. Specifically, Sukuk issuers need to pay attention to the return of asset, current ratio, growth and structure. On the other hand, bond issuers need to consider depth to equity, structure and maturity. As for investors, the findings of this study reveal that both bond and sukuk ratings reflect their performance.

This study provides useful information for investors that allows them to assess the risk of sukuk or bonds chosen based on rating and financial performance.

The novelty of this study lies in its econometric methodology used to identify factors which influence sukuk and bond ratings. Specifically, this study used two different techniques that allow a robust conclusion to be drawn. Furthermore, this study provides a systematic analysis which allows comparison between factors which affect bond and sukuk ratings in Indonesia.

Despite a growing interest in business analytics (BA) from the business and academic communities, it is still unknown what truly motivates and hinders the adoption of BA. To have a clear picture of what will lead to the successful implementation of BA, this paper identifies contextual variables (e.g. user characteristics, organizational readiness and technology infrastructure/expertise) that significantly influence the BA adoption decision.

This paper conducted a series of classification, discriminant and logistics regressions analyses to analyze the differences in mail survey responses between adopters and nonadopters of BA and then determine what either motivate or inhibit the BA adoption.

Through a series of hypothesis testing, we discovered that large firms with a greater number of information technology (IT) staff and budget tended to adopt BA more than their smaller counterparts. Also, we found that BA skeptics, who did not fully recognize BA benefit potentials, were more concerned about BA implementation costs and experienced the greater organization resistance to BA adoption than the others did. Therefore, they were less likely to adopt BA.

In the era of knowledge-based economy, the firm's ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilizing BA which is designed to help business executives and policymakers make well-thought and informed decisions. This paper is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the exploratory study of BA practices among the Korean firms.

This study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to detect the outliers and influential observations of the data from experimental data.

The proposed statistical approach is applied to analyze some experimental data on internal solitary wave propagation.

A suitable logistic regression model in which the relationship between the response variable and the explanatory variables is found. The problem of multicollinearity is tested. It was found that certain observations would not have the problem of multicollinearity. The P‐values for both the Pearson and deviance χ² tests are greater than 0.05. However, the Pearson χ² value is larger than the degrees of freedom. This finding indicates that although this model fits the data, it has a slight overdispersion. After three outliers and influential observations (cases 11, 27, and 49) are removed from the data, and the remaining observations are refitted the goodness‐of‐fit of the revised model to the data is improved.

A comparison of the four predictive powers: R², max‐rescaled R², the Somers' D, and the concordance index c, shows that the revised model has better predictive abilities than the original model.

The goodness‐of‐fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model.

Emergency health and humanitarian nonprofits work under volatile circumstances that strain nonprofits' financial resources. This study investigates the impact of revenue composition on the financial health of these nonprofits and the impact of financial health on the likelihood of financial distress.

A sample of 11,335 emergency nonprofits from 2003 to 2020 was obtained through form 990 data and studied through a difference generalized method of moments (GMM) approach for the impact of revenue composition on financial health. The impact of financial health on financial distress was studied through panel logistics regression.

Revenue diversification adversely affects the financial health of nonprofit emergency health and humanitarian organizations contrary to the implications of modern portfolio theory. The financial health of nonprofit emergency health and humanitarian organizations is persistent through the significant positive effect of lags in most cases.

The emergency health subsector of nonprofits was studied separately due to the unique nature of the sectors' operations and operating environment. The impact of revenue composition was investigated on key dimensions of financial health. Omitted variable bias, simultaneity and dynamic endogeneity were handled through difference GMM.

To help firms with their international operations, governments often create policies and support mechanisms, but its influence on the firm's exit decision has so far been ignored. Hence, the purpose of this study is to examine the impact of home-country governmental support on the firm's exit decision.

The authors test their conceptual model using multiple informants as well as secondary data from China. The sample consists of 360 valid questionnaires from 180 firms. Binary logistics regression is used to test the conceptual framework.

By demonstrating that resource-based and institutional constructs are highly dependent, the authors show how home-country governmental support interacts with the foreign affiliate's past performance to explain the decision to remain or exit a foreign market. The results indicate that while governmental financial support reduces the likelihood of exiting a poorly performing business in the foreign market, governmental non-financial support surprisingly has an opposite effect.

While there has been an increasing number of firms exiting foreign markets, this area of research is still limited. The study also contributes to the literature by focusing on home-country governmental financial and non-financial support to explain the firm's exit decision – an issue that has been ignored and is expected to be particularly relevant for firms from emerging economies.

This integrative review aimed to identify and synthesis literature on analysis techniques and methodological approaches used to analyse consumer measures in mental health research.

The review included papers published up to January 2020 across seven databases: CINAHL, Web of Science, Medline, PsycINFO, EMBASE, Scopus and Google Scholar. Data search and extraction was conducted according to the recommended guidelines for conducting review by Cochrane and Joanna Briggs Institute. Mixed method synthesis was used to integrate both qualitative and quantitative data into a single synthesis.

The initial search yielded a total of 2,282 papers. A total of 32 papers were included in the synthesis. Most of the included papers (25/32; 78.12%) focused on psychometric properties, whereas 14% (5/32) targeted analysis techniques, and 6.3% (2/32) addressed methodological justification. The measurement models (e.g. psychometric properties) were analysed through validity and reliability testing as part of instrument development and adaptation. The structural models were analysed using techniques such as structural equation modelling, multivariable regression models, intraclass correlation coefficient and partial least squares–structural equation modelling.

Although consumer-reported instruments are analysed using techniques involving linear, hierarchical and longitudinal effects, no attempt has been given to procedures that applied complex data mining or machine learning. Consumer researchers, clinicians and quality management are encouraged to apply rigorous analysis techniques to critically evaluate consumer outcome measures.

This review provides evidence on the analysis techniques in mental health research to inform the training of mental health professionals, students and quality assessment practitioners.

This paper aims to understand how guests’ trip purpose and hotel star rating influence the effects of the value for money perceived at hotels and service quality on guest satisfaction and word of mouth (WOM) recommendation.

Using TripAdvisor, 25 Singaporean hotels were randomly selected for the study, which yielded hotel reviews from 2,040 respondents. Hierarchical and logistic regression analysis was conducted to investigate the relationships proposed in the study.

Results indicate significant differences between leisure and business guests’ perception of value for money and service quality at hotels with various star ratings. While perceived value for money and service quality were found as significant predictors for both leisure and business guests’ satisfaction and WOM, the effects were moderated by the hotel star rating. Despite the significant effect of hotel star rating on guest satisfaction, the study found no significant relationships between hotel star rating and WOM for leisure and business guests.

The findings suggest that managers in the hotel industry should understand the purpose of guests’ trip and offer services based on their expectations. As the star rating of a hotel creates certain expectations for both leisure and business guests, providing an appropriate level of services and assuring value for money in accordance with the hotel rating contributes to guest satisfaction and WOM recommendation.

This study contributes to the hospitality literature by investigating how hotel star rating moderates the relationship of value for money and service quality on leisure and business guests’ satisfaction and WOM recommendation.

Longitudinal facial cracks (LFC) are one of the major defects occurring in the continuous-casting stage of thin slab caster using funnel molds. Longitudinal cracks occur mainly owing to non-uniform cooling, varying thermal conductivity along mold length and use of high superheat during casting, improper casting powder characteristics. These defects are difficult to capture and are visible only in the final stages of a process or even at the customer end. Besides, there is a seasonality associated with this defect where defect intensity increases during the winter season. To address the issue, a model-based on data analytics is developed.

Around six-month data of steel manufacturing process is taken and around 60 data collection point is analyzed. The model uses different classification machine learning algorithms such as logistic regression, decision tree, ensemble methods of a decision tree, support vector machine and Naïve Bays (for different cut off level) to investigate data.

Proposed research framework shows that most of models give good results between cut off level 0.6–0.8 and random forest, gradient boosting for decision trees and support vector machine model performs better compared to other model.

Based on predictions of model steel manufacturing companies can identify the optimal operating range where this defect can be reduced.

An analytical approach to identify LFC defects provides objective models for reduction of LFC defects. By reducing LFC defects, quality of steel can be improved.

The purpose of this study is to identify the specific skills and abilities within the broad category of data analytics that current business professionals believe are most important for accounting graduates. Data analytics knowledge is clearly important, but this category is broad. Therefore, this study identifies the specific skills and abilities that are most important for accounting graduates so that faculty can create classroom materials most beneficial for the future accounting graduates. In 2013, the Association to Advance Collegiate Schools of Business developed new standards for accounting programs, including standard A7, related to information technology and analytics. The intent of the standard clearly focuses on increasing the level of technology and analytics studied within the accounting curriculum. However, the specific details and methods for achieving the intent of A7 remain an open question. This chapter uses prior research focused on business analytics education to identify potential analytic skills, tools, techniques, and management issues of concern within the accounting profession. A survey of 342 accounting professionals identifies suggested areas of analytic competencies for accounting graduates. Specifically, the authors find preferences for skills related to data interpretation and communication over any individual technical skills or statistical knowledge. These skills suggest a role for accountants as intermediaries who may need to translate analytic activities into business language. Post hoc, the authors examine the survey results for differences based on respondent characteristics. Interestingly, female respondents report lower beliefs about the importance of analytic skills. The authors also find some differences when examining different demographics within the respondents.