大数据、人工智能与厂商竞争路径

经济学对市场竞争路径的学理性分析，主要集中在价格确定、产量确定、规模经济、产业组织等方面，而对科技进步引发市场竞争路径的变化并没有足够的关注。其实，市场竞争路径变化的底蕴是科技进步，只是经济学家在分析市场竞争路径时偏好于将科技因素作为外生变量处理。大数据和人工智能等的发展可谓是一场史无前例的科技革命，它对人类经济活动产生广泛而深刻的影响主要表现为：大数据及其运用怎样影响厂商投资经营，大数据与机器学习等人工智能手段相融合会在哪些方面改变厂商竞争路径，厂商如何提高数据智能化和实现网络协同化，在什么样的条件下会出现行业垄断，等等。文章的基本分析观点是：厂商竞争路径变化是贯穿于大数据、互联网和人工智能等相互融合过程的一种现象，这种现象对应于新科技进步和运用的不同层级；微观经济分析需要将新科技因素作为内生变量，通过分析大数据、机器学习与厂商竞争路径之间的关联，揭示厂商竞争路径变化机理以及由此引致的产业组织等问题。     

Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.     

How to move away from the silos of business management education?

Business management education is criticized for being too theoretical and fractional. Despite the numerous efforts to build integrated and experiential business curricula, learning is still organized in disciplinary silos. The curriculum integration efforts are carried out in separate sections of the curriculum rather than the core. There are theoretical, holistic models, but a lack of concrete examples of holistic business curriculum implementations. The authors bring the separate sections together by developing a holistic core curriculum model with three perspectives: a structure to bring intellectual coherence, people organized in learning communities, and an enterprise resource planning–supported learning environment to bring the practical training ground. The authors present a concrete implementation in a case study with first-year undergraduate business students and present their lessons learned.     

Differentiating two types of learning in contract design: Evidence from the construction industry

This research investigates the learning of inter-organizational contract design in greater depth. Two types of learning, i.e. learning from all past partnerships and learning from one specific partner, are distinguished in terms of their influence on the complexity of three different functions of the contract, namely control, coordination, and adaptation. Contract design capability and interorganizational routines are employed as mediators to explain the two types of learning respectively. Empirical tests using data from the Chinese construction industry reveal that there are significant indirect effects between partner-specific experience and contractual coordination, and between general partnership experience and all the three functions of the contract. This research contributes to the literature by providing more nuanced conclusions regarding the contract learning issue.     

Is brain hemisphericity a correlate of performance in problem-solving courses? A case study from an HBCU

The authors set out to determine the brain dominance characteristics of students enrolled in business statistics courses in a historically Black university in a major southeastern Texas city. Thereafter, the authors investigated the relationship between a student’s brain hemispheric preference and academic performance in college courses, which emphasize problem solving. The results reported in this study reveal that left brain– and right brain–dominant students are at parity when it comes to performance in a problem-solving course. Moreover, both left brain– and right brain–dominant learners perform better than whole-brain dominant learners in a problem-solving course.     

The impact of German-speaking online media on tourist arrivals in popular tourist destinations for Europeans

This empirical study analyzes the relationship between the sentiments in online media with regard to travel destinations and corresponding tourist arrivals. We expect the media reports on political and economic instability and turmoil to enhance tourist arrival nowcasts and forecasts, as they can probably complement them with information on disruptions and shocks. Therefore, we believe this research will help to build better models for tourism demand nowcasting and forecasting. We use the sentiment in the German-speaking online media because the German-speaking region is the most populated in Europe and has the largest group of travelers visiting destinations in and around Europe.

An artificial neural network is used to analyze the mood of the media. The software classifies news items regarding potential tourist destinations with either positive or negative labels. The number of positive and negative news items is used to build sentiment indices for popular tourist destinations for Europeans.

Our results show strong correlations between the mood concerning tourist destinations and tourist arrivals in these countries. Indeed, disruptions and shocks prevalent in the news are reflected in similar ratios in both tourist arrivals and sentiment indices. These results can be used as a new explanatory variable for tourism demand modelling.     

Bayesian forecasting of electoral outcomes with new parties’ competition

We propose a new methodology for predicting electoral results that combines a fundamental model and national polls within an evidence synthesis framework. Although novel, the methodology builds upon basic statistical structures, largely modern analysis of variance type models, and it is carried out in open-source software. The methodology is motivated by the specific challenges of forecasting elections with the participation of new political parties, which is becoming increasingly common in the post-2008 European panorama. Our methodology is also particularly useful for the allocation of parliamentary seats, since the vast majority of available opinion polls predict at national level whereas seats are allocated at local level. We illustrate the advantages of our approach relative to recent competing approaches using the 2015 Spanish Congressional Election. In general, the predictions of our model outperform the alternative specifications, including hybrid models that combine fundamental and polls models. Our forecasts are, in relative terms, particularly accurate in predicting the seats obtained by each political party.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Thinking critically about critical thinking: Assessing critical thinking of business students using multiple measures

Critical thinking is a skill that potential employers expect all graduates to possess. Hence, most business management programs consider critical thinking as an important student learning goal. Unfortunately, there is ambiguity about how to best assess critical thinking, both as a skill and a learning outcome. The authors empirically demonstrate how they measure the critical thinking ability of their students in different settings, and how their critical thinking ability improves as they progress through the business program.     

Factors affecting classroom participation and how participation leads to a better learning

The purpose of this paper is twofold. First, we analyze various course- and student-related factors that affect participation grades within several college accounting classes. The second purpose is to determine whether in-class participation grades have an effect on exam performance in these classes. We find that, unlike in liberal arts and sciences courses, in accounting courses, participation grades for female students do not differ from participation grades for their male counterparts, regardless of class level. Overall, course level and student major have no direct effect on participation grades; however, the accounting majors and students in the lower level courses have better attendance than do non-accounting majors and students in more advanced courses. We observe that class schedule, in particular class duration and semester, affects students’ participation performance. Lastly, we find that students who participate more in classroom discussions perform 25% better on exams than do those with lower participation grades. This relationship holds for the three elements of participation grading: frequency of participation, consistency of participation, and attendance.