Collective Intelligence Under a Volatile Task Environment: a Behavioral Experiment Using Social Networks and Computer Simulations

Collective intelligence in our highly-connected world is a topic of interdisciplinary interest. Previous research has demonstrated that social network structures can affect collective intelligence, but the potential network impact is unknown when the task environment is volatile (i.e., optimal behavioral options can change over time), a common situation in modern societies. Here, we report a laboratory experiment in which a total of 250 participants performed a “restless” two-armed bandit task either alone, or collectively in a centralized or decentralized network. Although both network conditions outperformed the solo condition, no sizable performance difference was detected between the centralized and decentralized networks. To understand the absence of network effects, we analyzed participants’ behavior parametrically using an individual choice model. We then conducted exhaustive agent-based simulations to examine how different choice strategies may underlie collective performance in centralized or decentralized networks under volatile or stationary task environments. We found that, compared to the stationary environment, the difference in network structure had a much weaker impact on collective performance under the volatile environment across broad parametric variations. These results suggest that structural impacts of networks on collective intelligence may be constrained by the degree of environmental volatility.

Tobacco tax evasion in Western Balkan countries: tax evasion prevalence and evasion determinants

Background and objectiveTobacco tax evasion undermines the goal of tobacco taxes as a tobacco control measure to make tobacco products less affordable, increases the health risks for those who smoke and decreases the government revenue. This paper analyses the tobacco tax evasion in six Western Balkan (WB) countries: Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia and Serbia. The aim of this research is to estimate the size of the illicit market and identify the main determinants of tax evasion activities in the Southeastern European region.Data and methodsData from 2019 Survey on Tobacco Consumption in Southeastern Europe (STC-SEE) are used. STC-SEE provides uniquely comparable nationally representative data on smoking behaviour for adult (18–85 years old) population for each country. Tax evasion is defined on the basis of available information on tax stamps, health warnings, price and the place of purchase, in accordance with the previous research on tax evasion. In order to estimate the determinants of illicit purchases we use binary choice model of tax evasion.ResultsThe study finds that 20.4% of all current smokers in WB countries evade taxes on tobacco products, with evasion being much more frequent for hand-rolled (HR) tobacco (86.7%) than for the manufactured cigarettes (MC) (8.6%). While HR is predominantly illicit in all six countries, MC evasion varies significantly, with evasion being significantly higher in Montenegro and Bosnia and Herzegovina. Results further suggest that tax evasion is higher in the statistical regions where institutional capacities to tackle illicit trade are lower, in municipalities bordering countries with high MC evasion, as well as among smokers with low income, women and elderly. We also provide evidence that higher tobacco taxes and prices do not increase illicit consumption.ConclusionThe findings from the research suggest that in order to decrease tax evasion, governments should put additional effort to strengthen institutional capacities to tackle illicit tobacco markets. Furthermore, improving regional coordination in development and implementation of tobacco control policies, including the prevention of illicit market, is essential in lowering evasion in all WB countries. Finally, WB countries should regulate and enforce excise tax stamp requirements on the HR tobacco market to a much higher degree.

The Insertion of Economic Cybernetics Students on the Romanian Labor Market in the Context of Digital Economy and COVID-19 Pandemic

The purpose of this paper is to provide evidence of the insertion of Romanian students of economic cybernetics on the labor market by connecting business environment expectations with the goals of a competitive digital economy. The research is organized around three hypotheses to address the issues of both non-employed and employed economic cybernetics students. A rank-ordered probit choice model was estimated to compute the probability that a certain skill requires improvement. The empirical results showed that the COVID-19 pandemic stimulated more cybernetics students to get a job in this period. Moreover, these students present the necessary level of digital skills to be employed, but other skills need improvement: skills of analysis and synthesis, adaptability in handling crisis situations and creativity. This research reveals the lack of working experience as the main cause for rejection after an interview and the students’ tendency to overestimate their salary. This study also identified barriers of the insertion on the labor market for these students with digital skills that were not the subject of previous studies. Moreover, the impact of the COVID-19 pandemic on their decision to get a job in this period is assessed and a few recommendations of skills improvements are provided. These results present practical implications for educational policies and the business environment in the context of achieving a competitive European digital economy. The limit of this research is given by the sample representativeness for cybernetics students only for Bucharest, but a future paper will ensure a representative sample at the national level.

Logistics Performance, Ratings, and Its Impact on Customer Purchasing Behavior and Sales in E-Commerce Platforms

Problem definition: We examine the impact of logistics performance metrics such as delivery time and customer’s requested delivery speed on logistics service ratings and third-party sellers’ sales on an e-commerce platform. Academic/practical relevance: Although e-commerce retailers like Amazon have recently invested heavily in their logistics networks to provide faster delivery to customers, there is scant academic literature that tests and quantifies the premise that convenient and fast delivery will drive sales. In this paper, we provide empirical evidence on whether this relationship holds in practice by analyzing a mechanism that connects delivery performance to sales through logistics ratings. Prior academic work on online ratings in e-commerce platforms has mostly analyzed customers’ response to product functional performance and biases that exist within. Our study contributes to this stream of literature by examining customer experience from a service quality perspective by analyzing logistics service performance, logistics ratings, and its impact on customer purchase probability and sales. Methodology: Using an extensive data set of more than 15 million customer orders on the Tmall platform and Cainiao network (logistics arm of Alibaba), we use the Heckman ordered regression model to explain the variation in customers’ rating of logistics performance and the likelihood of customers posting a logistics rating. Next, we develop a generic customer choice model that links the customer’s likelihood of making a purchase to the logistics ratings provided by prior customers. We implement a two-step estimation of the choice model to quantify the impact of logistics ratings on customer purchase probability and third-party seller sales. Results: We surprisingly find that even customers with no promise on delivery speed are likely to post lower logistics ratings for delivery times longer than two days. Although these customers are not promised an explicit delivery deadline, they seem to have a mental threshold of two days and expect deliveries to be made within that time. Similarly, we find that priority customers (those with two-day and one-day promise speed) provide lower logistics ratings for delivery times longer than their anticipated delivery date. We estimate that reducing the delivery time of all three-day delivered orders on this platform (which makeup [Formula: see text] 35% of the total orders) to two days would improve the average daily third-party seller sales by 13.3% on this platform. The impact of delivery time performance on sales is more significant for sellers with a higher percentage of three-day delivered orders and a higher spend per order. Managerial implications: Our study emphasizes that delivery performance and logistics ratings, which measure service quality, are essential drivers of the customer purchase decision on e-commerce platforms. Furthermore, by quantifying the impact of delivery time performance on sales, our study also provides a framework for online retailers to assess if the increase in sales because of improved logistics performance can offset the increase in additional infrastructure costs required for faster deliveries. Our study’s insights are relevant to third-party sellers and e-commerce platform managers who aim to improve long-term online customer traffic and sales.

Exploring Travel Mode Preference of External Trips for Smart City Transportation Planning: Sejong, Korea

In the face of growing concerns about urban problems, smart cities have emerged as a promising solution to address the challenges, for future sustainable societies in cities. Since the early 2000s, 67 local governments in Korea have been participating in smart city projects, as of 2019. The Sejong 5-1 Living Area smart city was selected as one of two pilot national demonstration smart cities. The main objectives of this study are to introduce the Sejong 5-1 Living Area smart city project that is currently in the planning stage, present travel and mode preferences focusing on external trips in a smart city context to be built, and analyze a mode choice model according to the socioeconomic characteristics of individual travelers. One of the distinguishing features of the Sejong smart city is its transportation design concept of designating a sharing car-only district within the city to limit private vehicle ownership to about one-third of residents, while bus rapid transit (BRT) plays a central role in mobility for external trips among four transport modes including private cars, BRT, carsharing, and ridesharing. This study was analyzed using the stated preference survey data under hypothetical conditions by reflecting the unique characteristics of the Sejong smart city transportation policy. Approximately two-thirds of respondents in the survey preferred to spend less than 1.25 USD, traveling less than 35 min on BRT trips. On the basis of the survey data, we developed a mixed logit mode choice model and found the overall model estimates to be statistically significant and reasonable. All people-specific variables examined in this study were associated with mode choices for external commuting trips, including age, income, household size, major mode, driving ability, and presence of preschoolers.

Big Data Analytics and Discrete Choice Model for Enterprise Credit Risk Early Warning Algorithm

A business credit risk early warning algorithm based on big data analysis and discrete selection model is presented to address the issues of poor sample fitting performance, long warning time, and low warning accuracy that plague the traditional enterprise credit risk early warning algorithm. A-share listed enterprises in China were chosen as the credit data source for screening the samples based on big data analysis. After screening, financial failure firms were coupled, and paired samples were created. The credit risk variables, which included financial and corporate governance characteristics, were chosen based on the created samples. The enterprise financial risk submodel and the nonfinancial risk submodel were built based on the enterprise credit risk variables, and the financial and nonfinancial index scores of enterprise customers were evaluated separately to develop a discrete choice model of enterprise credit risk. The algorithm’s sample fitting performance was employed to achieve early warning of corporate credit risk. The algorithm based on big data analytics and discrete choice model is compared to the traditional method in order to verify its validity. The findings of the experiment reveal that the algorithm’s sample fitting performance is superior to the traditional one, making it more suitable for enterprise credit risk early warning. The proposed model depicts 85% accuracy.

Environmental drivers of autumn migration departure decisions in midcontinental mallards

Background The timing of autumn migration in ducks is influenced by a range of environmental conditions that may elicit individual experiences and responses from individual birds, yet most studies have investigated relationships at the population level. We used data from individual satellite-tracked mallards (Anas platyrhynchos) to model the timing and environmental drivers of autumn migration movements at a continental scale. Methods We combined two sets of location records (2004–2007 and 2010–2011) from satellite-tracked mallards during autumn migration in the Mississippi Flyway, and identified records that indicated the start of long-range (≥ 30 km) southward movements during the migration period. We modeled selection of departure date by individual mallards using a discrete choice model accounting for heterogeneity in individual preferences. We developed candidate models to predict the departure date, conditional on daily mean environmental covariates (i.e. temperature, snow and ice cover, wind conditions, precipitation, cloud cover, and pressure) at a 32 × 32 km resolution. We ranked model performance with the Bayesian Information Criterion. Results Departure was best predicted (60% accuracy) by a “winter conditions” model containing temperature, and depth and duration of snow cover. Models conditional on wind speed, precipitation, pressure variation, and cloud cover received lower support. Number of days of snow cover, recently experienced snow cover (snow days) and current snow cover had the strongest positive effect on departure likelihood, followed by number of experienced days of freezing temperature (frost days) and current low temperature. Distributions of dominant drivers and of correct vs incorrect prediction along the movement tracks indicate that these responses applied throughout the latitudinal range of migration. Among recorded departures, most were driven by snow days (65%) followed by current temperature (30%). Conclusions Our results indicate that among the tested environmental parameters, the dominant environmental driver of departure decision in autumn-migrating mallards was the onset of snow conditions, and secondarily the onset of temperatures close to, or below, the freezing point. Mallards are likely to relocate southwards quickly when faced with snowy conditions, and could use declining temperatures as a more graduated early cue for departure. Our findings provide further insights into the functional response of mallards to weather factors during the migration period that ultimately determine seasonal distributions.

A Strategic Model to Promote University of Choice Decisions Among the International Students

The internationalisation of higher education has become increasingly important for many higher education institutions (HEIs) globally. To recruit national and international students, HEIs must invest in effective digital marketing and recruitment strategies. This study investigated the development of a strategic university of choice model that can assist universities in the recruitment of international students. A survey was completed by 306 international students studying at a South African university. The factors identified in this study included academic programme and quality, visa requirements, country/city attractiveness, lectures in English, costs, student life, safety and security, university location, university reputation, and assistance from the international office. The strategic university of choice model could assist university marketing personnel to develop a focused, targeted, and cost-effective digital marketing and recruitment strategy to recruit international students.