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The purpose of this paper is to detect the existence of price bubbles and examine the possible contributing factors that associate with price bubble occurrences in China agricultural commodity markets.

Using recently developed rolling window right-side augmented Dickey–Fuller test, we first detect the dates of price bubbles in China's two important agricultural commodity markets, namely corn and soybeans. Then, we use a penalized maximum likelihood estimation of a multinomial logistic model to estimate the contributing factors of price bubbles in both markets, respectively.

Results from the bubble detection indicate that price bubbles account for 5.48% (3.91%) of the studied periods for corn (soybeans). More importantly, we find that market liquidity and speculation have opposite effects on the occurrences of bubbles in the corn and soybeans market. World stocks-to-use and exchange rates affect the occurrences of bubbles in a different way for each commodity, as well. Price bubbles are more likely associated with strong economic activity, high interest rates and low inflation levels.

This is the first study considering commodity-specific features into the formation of price bubbles. Through accurately identifying the bubble dates and fixing the estimation bias of rare events models, this study enables us to obtain robust results for each commodity. The results imply that China's corn and soybeans market respond differently to the speculative activity and external shocks from international markets. Therefore, future policy regulations on commodity markets should focus on more commodity-specific factors when aiming at avoiding bubble occurrences.

Alcohol and tobacco consumption are significant public health concerns and considered some of the riskiest behaviors among students. Despite strong indications of heavy consumption of these substances, certain sections of the student population remain unexplored regarding their consumption patterns and associated factors, including research scholars pursuing Doctor of Philosophy degrees. This study aims to explore the patterns and correlations of consumption of alcohol and tobacco among doctoral students in three Indian universities.

A cross-sectional study was conducted on a total of 530 scholars from three central universities, Aligarh Muslim University, Banaras Hindu University and Jawahar Lal Nehru University, and focused on two substances (alcohol and tobacco) frequently used by students in India. Bi-variate analysis and penalized logistic regressions were applied to analyse the prevalence and associated factors of alcohol, smoking and smokeless tobacco among doctoral students.

Tobacco consumption was observed to be the dominant form of substance use among doctoral students. Findings revealed that 34% of scholars smoked and 25% consumed smokeless tobacco daily and around 14% consumed alcohol at least once a week. Major significant correlates of substance consumption among scholars were found to be gender, religion, parental mortality, dissatisfaction with academic performance and stress about future career/employment. Significant variation in substance use patterns was observed among universities.

This study provides first-hand evidence on substance use patterns and the correlates among doctoral students in three Indian universities. Findings indicate the implementation of specific intervention programs for mental health and counselling for this section of students to prevent substance use addiction would be beneficial.

Built upon three components (attitudes, subjective norms, and perceived behavioral control) of the theory of planned behavior (TPB), the purpose of this paper is to analyze the factors that influenced accounting students’ intention to pursue public accounting instead of private accounting as their career choice in China.

The TPB is used to predict the rational intention of accounting students in this study. A binary logistic regression analysis was applied to test all hypotheses because of its suitability.

The authors find that the variety and marketability of public accounting experiences, high turnover plus, low firm cohesion in the workplace, and perceived difficulties in traveling extensively affected students’ intentions to pursue the public accounting profession significantly. By highlighting these factors that affect students’ intention to pursue public accounting, this paper has important implications for the accounting educators and other stakeholders in China and other emerging economies.

By understanding the factors influencing the accounting students’ interests in pursuing public accounting, accounting educators and accounting firms could make some changes, redesign accounting curriculum, and enhance internship experience and recruiting process in order to increase more interests in public accounting. Professional organizations and policy makers might learn some lessons to take some actions to encourage young accounting professionals to work for public accounting firms. Moreover, this study has provided a valuable perspective to accounting educators, practitioners, and policy makers in other emerging economies with the same shortage of qualified public accountants.

The concept of style is gaining momentum in organizational research. Focussing on its implications for strategy, this paper presents a conceptual and methodological framework to make the notion of style operational and applicable to both research and practice. Style is defined here as a combinatorial, socially situated and semiotic device that can be organized into typologies – recurrent combinations of stylistic dimensions exerting a normative and semiotic function within and across contexts. The empirical analysis, situated in the field of electronic music, considers the music genres and the colour dimension of artists' appearance as components of their style. Results show how coherent style typologies normatively dominate the field and how non-conformist but coherent typologies correspond to superior creative performance. Operating as unifying device, style can transform varied and potentially confounding traits into distinctiveness and shed light on competitive market dynamics that cannot be fully explained via other theoretical constructs.

The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment.

Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can solve a mathematics problem correctly based on how well they solved other problems in the past. The usefulness of the model was evaluated by comparing the predicted probability of correct problem solving to the actual problem solving performance on the data set that was not used in the model building process.

The regularized logistic regression model showed a better predictive power than the standard Bayesian Knowledge Tracing model, the most frequently used quantitative model of student learning in the Educational Data Mining research.

Providing instructional scaffolding is critical in order to facilitate student learning. However, most computer-based learning environments use heuristics or rely on the discretion of students when they determine whether instructional scaffolding needs be provided. The predictive model of problem solving performance of students can be used as a quantitative guideline that can help make a better decision on when to provide instructional supports and guidance in the computer-based learning environment, which can potentially maximize the learning outcome of students.

Receiving Michelin stars is considered an effective marketing tool and a prestigious certification in the restaurant industry. However, the direct effects of Michelin stars on restaurant performance remain unclear. To bridge this gap, this study aims to empirically validate the “Michelin effect” on the consumption values of restaurant patrons before and after receiving Michelin stars.

The data, collected from OpenTable, consist of over 160,000 reviews written for 218 restaurants, including 109 Michelin-starred (treatment group) and 109 nonstarred restaurants (control group). The authors measure perceived consumption value using the collected user-generated review data. The authors estimate fixed-effect difference-in-differences regressions to validate the Michelin effect.

Michelin stars enhance social, hedonic and service quality values, which are nonfunctional values. However, no significant effects on functional consumption values, such as economic, food quality and ambience quality values, are observed, even though Michelin stars are pure awards for kitchen performance.

Michelin stars can be an effective marketing tool for fine-dining restaurants because customers consider emotional and nonfunctional benefits such as hedonic and social values, which are more important than functional benefits such as food value. Furthermore, Michelin effects are heterogeneous depending on the number of stars awarded, price range and customers’ gastronomic involvement. These offer a strong rationale for monitoring social media, which may help managers better understand their customers and improve their performance.

This study extends the current literature on the Michelin effect by quantifying consumption values using user-generated review data. To the best of the authors’ knowledge, this study offers the first empirical evidence that directly validates the Michelin effect.

There is clear benefit in designing and sending notifications to users that persuade them to interact with an app and marketer goals. The purpose of this study is to examine how different motivational affordances in notifications affects subsequent app use.

The authors designed three studies to address the purpose: (1) an online experiment to test how individuals perceived notifications, which contained social affordances, progression-based affordances, and a combination of social and progression affordances; (2) a survey to gain a deeper understanding of why certain notification characteristics were effective and to unearth factors that jointly affected notification effectiveness; and (3) an in-app field experiment to test if the findings from studies 1 and 2 held up in a “real world” setting.

The analysis revealed that progression incentives yielded the greatest increases in user behavior. Neither a social incentive, nor a combination of social and progression affordances was more effective than one progression affordance. This effect was heightened by consumers’ involvement with the focal brand.

The contribution extends knowledge about the use of motivational affordances to gamify push notifications in high-involvement contexts. This implies that greater attention should be paid to how the: length of push notifications, affordances communicated and degree of consumers’ relationship with a focal brand (i.e. involvement) impact notification effectiveness. These findings set out new avenues to investigate the uses of gamification and services marketing in future research.

The authors provide marketers with insights into the most effective ways to gamify, structure and time the delivery of notifications. In high-involvement contexts where consumers decide whether to act on a gamified marketing affordance quickly, it pays to use push notifications that feature visible, immediate and tangible rewards. Understanding consumers’ involvement with the brand allows marketers to turn notifications from a potential annoyance into a viable conduit for engagement.

This research extends knowledge on gamification to the domain of push notifications. In doing so, the authors have demonstrated the communicated affordances and wording of the push notifications organizations send affect user behavior. The authors further expand knowledge of the role of consumer involvement on push notification effectiveness while controlling for app usage patterns.

Gene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.

The proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.

The proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.

This paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.

This purpose of this paper is to describe a fast and easy method of both clustering samples and identifying active genes in cDNA microarray data.

The method relies on alternation of identification of the active genes using a mixture model and clustering of the samples based on Ward hierarchical clustering. The initial‐point of the procedure is obtained by means of a χ² test. The method attempts to locally minimize the sum of the within cluster sample variances under a suitable Gaussian assumption on the distribution of data.

This paper illustrates the proposed methodology and its success by means of results from both simulated and real cDNA microarray data. The comparison of the results with those from a related known method demonstrates the superiority of the proposed approach.

Only empirical evidence of algorithm convergence is provided. Theoretical proof of algorithm convergence is an open issue.

The proposed methodology can be applied to perform cDNA microarray data analysis.

This paper provides a contribution to the development of successful statistical methods for cDNA microarray data analysis.

To demonstrate how awareness of Neo-Marxist critical theory and Neo-Weberian comparative–historical sociology would have been beneficial to U.S. policy planners and decision-makers, especially Presidents.

This study employs qualitative analysis of available sources rather than quantitative data analysis.

Based on its practical application to a specific historical instance, the heuristic value of Max Weber’s ideal-type model of traditional authority (Herrschaft [domination]) is confirmed, as it is apparent that Henry Kissinger’s interpretation of the meaning of Realpolitik harmed U.S. foreign policy.

There is an imminent need to be critical of claims to expertise by advisors of major decision-makers. The practical relevance of possessing an adequate grasp of a given situation as the context in which actors must make choices is evident, as applies with regard to the current crises facing the world, which must be approached and addressed as scrupulously as possible.

Prevailing critiques of Kissinger and American foreign policy have tended to accept the premise that Kissinger was well-informed and giving good advice based on extensive and appropriate scholarship. That was not the case in Vietnam, in Indonesia, or in other regions. There are no available studies that examine Kissinger’s Eurocentric and limited perspective in light of critical theory and comparative–historical sociology.