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The purpose of this manuscript is to quantify exactly how much information and/or predictive content is contained in business sentiment surveys.

This paper uses techniques drawn from information theory econometrics, and more specifically the theory of information entropy, to characterize the predictive content of business sentiment surveys. The authors apply these techniques to publicly available information obtained from various editions of the Federal Reserve Bank of New York's Empire State Manufacturing Survey, one of the most popular business sentiment surveys conducted in the USA. Parametric and non‐parametric statistical analyses are used to examine differences in the quantity of predictive content across various questions in the survey.

The results suggest that business sentiment surveys contain a reasonably high degree of informative content. However, the amount of informative content varies considerably from question to question in the survey. Questions that are more general in nature and ask about current perceptions (as opposed to future expectations) contain more informative content.

Business sentiment surveys are a practical, low‐cost method to assess the current and expected future state of local and regional economies. However, the value of these surveys is questionable if they do not contain much information. This research finds that such surveys do contain a large amount of information, and are worth administering. However, specific types of survey items convey more information that others, which also suggests that business sentiments surveys can be further revised to maximize the amount of content gained from respondents.

The purpose of this paper is to investigate the predictive factors of information seeking behavior of smartphone users from the cross-generational perspective. Based on existing literature, the two most popular types of information seeking behavior of smartphone users were determined: social information seeking behavior; and functional/cognitive information seeking behavior.

A questionnaire comprising 66 questions was administered online to 216 smartphone users of three age groups according to three generations: generation X, Y (millennials) and Z. Several predictive factors were examined for each of these information seeking behavior types: generation, gender, personality traits (the Big Five), daily usage time, period of ownership, various application utilization and the level of emotional gain from smartphones.

There is a trade-off between the two types of information seeking behavior. Also, men exhibited significantly more functional/cognitive information seeking behavior than women, and younger generations reported significantly higher emotional gain and social information seeking behavior than older generations. Interestingly, significant differences in smartphone apps’ utilization, information seeking behavior types and their predictive factors were found among users from different generations. Extraversion was positively related to social information seeking behavior only for generations X and Y, while WhatsApp usage was one of the strongest predictive factors only for generation Z.

This research has practical implications for information system design, education, e-commerce and libraries.

This is a first study that systematically examines predictive factors of the two prominent types of information seeking behavior on smartphones from the cross-generational perspective.

This paper examines the predictive content of financial variables above and beyond past GDP growth in a small open economy in the Eurozone. We aim to clarify potential differences in forecasting economic activity during periods of steady growth and economic turbulence.

The out-of-sample forecasting analysis is conducted recursively for two different time periods: the steady growth period from 2004:1 to 2007:4 and the financial crisis period from 2008:1 to 2011:2.

Our results from Finland suggest that the proper choice of forecasting variables relates to general economic conditions. During steady economic growth, the preferable financial indicator is the short-term interest rate combined with past growth. However, during economic turbulence, the traditional term spread and stock returns are more important in forecasting GDP growth.

The results highlight the importance of long-term interest rates in determining the level of the term spread when the central bank implements a zero interest rate policy. Moreover, during economic turbulence, stock markets are able to signal the expected effects of unconventional monetary policy on GDP growth.

This paper aims to explore the relationship between the characteristics of social media health information and its adoption. The purpose is to identify information characteristics that can be used to estimate the level of health information adoption in advance.

According to the Information Adoption Model (IAM), the study extracted ten information characteristics from the aspects of information quality and information source credibility. The sample data was collected from the top ten influential health accounts based on the Impact List of Sina Weibo to test the effectiveness of these characteristics in distinguishing information at different levels of adoption. The forecasting of information adoption level is regarded as a binary classification question in the study and support vector machine (SVM) is used to do the research.

The results indicate that ten information characteristics chosen in this study are related to information adoption. Based on these information characteristics, it is feasible to estimate the level of health information adoption, and the estimation accuracy is relatively high.

A lot of work has been done in previous researches to reveal the factors that influence information adoption. The theoretical contribution of this work is to further discuss how to use the influencing factors to do some predictive work for information adoption. In practice, it will help health information publishers to disseminate high-quality health information more effectively as well as promote the adoption of health information.

Information distortion affects the perception of quality, which, in turn, influences investment decisions and determines the pledge results of fundraising. This study combines signalling theory with persuasion theory to empirically study the effects of linguistic information distortion from fraudulent cues on a crowdfunding campaign's fundraising outcomes using text analytics, with implications for entrepreneurs, platforms and investors.

This study empirically analyzes 328,974 crowdfunding projects from the Kickstarter platform. Information distortion is detected using four indicators, based on text mining analytics. An econometric model is built to estimate the impact of information distortion, while the predictive power of the information distortion is detected through machine learning.

The results inform that distortion in the blurb, detailed description and reward statement dampen a campaign's success, but embellishing the entrepreneur's biography enhances the success of financing. Furthermore, information distortion exhibits a significant inverted U-shaped influence. The effect of the interaction terms suggests that campaigns with high pledge goals are more sensitive to information distortion, and that native-speaking entrepreneurs are adept at applying linguistic skills to promote the campaign.

This study provides a linguistic method to detect the influence of information distortion on crowdfunding campaigns. Further, the study offers some practical suggestions for entrepreneurs on how to generate attractive narratives, and contributes to the investor's decision-making and informs the platform's promotion strategy.

The purpose of this paper is to assess and compare the forecast ability of existing credit risk models, answering three questions: Can these methods adequately predict default events? Are there dominant methods? Is it safer to rely on a mix of methodologies?

The authors examine four existing models: O-score, Z-score, Campbell, and Merton distance to default model (MDDM). The authors compare their ability to forecast defaults using three techniques: intra-cohort analysis, power curves and discrete hazard rate models.

The authors conclude that better predictions demand a mix of models containing accounting and market information. The authors found evidence of the O-score's outperformance relative to the other models. The MDDM alone in the sample is not a sufficient default predictor. But discrete hazard rate models suggest that combining both should enhance default prediction models.

The analysed methods alone cannot adequately predict defaults. The authors found no dominant methods. Instead, it would be advisable to rely on a mix of methodologies, which use complementary information.

Better forecasts demand a mix of models containing both accounting and market information.

The findings suggest that more precise default prediction models can be built by combining information from different sources in reduced-form models and combining default prediction models that can analyze said information.

The purpose of this paper is to examine the long-term cointegrating relationship between ocean, rail, truck and air cargo freight rates, as well as the short-term dynamics between these four series. The authors also test the predictive ability of these freight rates on major economic indicators.

The authors employ a vector error-correction model using 16 years of monthly time series data on freight rate data in the ocean, truck, rail and air cargo sectors to examine the interrelationship between these series as well as their interrelationship with major economic indicators.

The authors find that truck freight rates and as well as dry bulk freight rates have the strongest predictive power over other transportation freight rates as well as for the four major economic indicators used in this study. The authors find that dry bulk freight rates lead other freight rates in the short-run but lag other freight rates in the long run.

While ocean freight rate time series have been examined in a large number of studies, little research has been done on the interrelationship between ocean freight rates and the freight rates of other modes of transportation. Through the use of data on five different freight rate series, the authors are able to assess which rates lead and which rates lag each other and thus assist future researchers and practitioners forecast freight rates. The authors are also one of the few studies to assess the predictive power of non-ocean freight rates on major economic indicators.

This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions.

Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns.

Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns.

The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy.

The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement.

The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.

The firms use training evaluation practices (TEPs) to determine the return of billions of dollars spent on employee training and development activities. The firms need to modernize the set of TEPs for evidence-based workforce management decisions. This study aims to examine a mediation mechanism to explain how human resource (HR) professionals’ design thinking (DT) mindset strengthens the set of TEPs using predictive workforce analytics (PWAs).

The authors used SPSS computational named MLMED to test the proposed relationships by collecting data from 180 management professionals serving in subsidiaries of multinational corporations in Pakistan.

The statistical results demonstrated that DT is not directly related to firms’ TEPs. However, the statistical results supported the mediating role of firms’ use of PWAs between DT and TEPs.

The findings offer a new perspective for firms to use HR professionals’ DT mindset for modernizing the set of existing HR practices.

Building supply chain (SC) resilience has become a priority for many organizations, following a global increase in disruptive events. While management accounting and control (MAC) systems play a supportive role in supply chain management (SCM) decisions, little is known about the contributions offered to resilience decisions in service organizations. The purpose of this study is to examine the performance implications of MCS's impact on proactive and reactive resilience of healthcare supply chains.

This study conducted a survey of 127 public health managers via structural equation modeling. The partial least squares version 3.3.3 was used.

The results show a statistically positive impact of MAC dimensions on proactive and reactive resilience, which in turn impacts the quality, delivery speed and cost effectiveness of the health SC. However, the integration dimension had an insignificant effect on reactive resilience but a positive effect on proactive resilience.

This study examined the performance implications of MAC system dimensions and proactive and reactive resilience on operational performance in health SCs, using empirical data from only one country. Thus, generalizing the findings to include other jurisdictions may be impossible.

Healthcare managers in public health facilities should embrace the four MAC dimensions (except the integrated dimension in reactive resilience) to support information generation in SC resilience decisions.

Perhaps, the first to provide preliminary empirical evidence on the interactive effect of proactive and reactive resilience and MAC dimensions in terms of broad scope, timeliness, integration and aggregation on health SC operational performance under disruption, in the context of an emerging economy.