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Mauritius is a Small Island Development State (SIDS) with limited resources, and it has been witnessed that many containers used for storing household and industrial products are made from plastic. When discarded as waste, those plastic containers pose a serious environmental and economic challenge for Mauritius. Moreover, landfill space is getting increasingly scarce, and plastic waste is contaminating both land and water. Therefore, it is of the utmost necessity to develop solutions for Mauritius' plastic wastes. Due to its abundance and accessibility, plastic waste is a promising material for recycling and energy production. One potential solution is the use of machine learning and artificial intelligence (AI) to predict household plastic consumption, allowing policymakers to design effective strategies and initiatives to reduce plastic waste. Such information is a critical component to be able to efficiently plan for the collection and routing of trucks when collecting recyclable plastics. The development of new strategies for the recycling of plastic waste and development of new industry can address the import and export potential of the country to achieve self-sustainability as well as contribute to reduction in plastic pollution and amount of waste landfilled. These plastics can thereafter be used effectively for recycling and for the making of 3D printing filaments which fall under the SDGs 9 (Industry, Innovation and Infrastructure) and 12 (Responsible consumption and production).

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

In this study, we ask whether economic factors that can be directly manipulated by public policy have important effects on the probability that women experience long-lasting unions. Using data from the 1979 National Longitudinal Survey of Youth, we estimate a five-stage sequential choice model for women's transitions between single with no prior unions, cohabiting, first-married, re-single (divorced or separated), and remarried. We control for expected income tax burdens, Aid to Families with Dependent Children (AFDC) or Temporary Assistance for Needy Families (TANF) benefits, Medicaid expenditures, and parameters of state divorce laws, along with an array of demographic, family background, and market factors. We simulate women's sequences of transitions from age 18 to 48 and use the simulated outcomes to predict the probability that a woman with given characteristics (a) forms a first union by age 24 and maintains the union for at least 12 years, and (b) forms a second union by age 36 and maintains it for at least 12 years. While non-policy factors such as race and schooling prove to have important effects on the predicted probabilities of long-term unions, the policy factors have small and/or imprecisely estimated effects; in short, we fail to identify policy mechanisms that could potentially be used to incentivize long-term unions.

The primary goal of this longitudinal study was to examine whether technology use predicts substance use and/or whether substance use predicts technology use during adolescence.

The sample included 1,031 10th and 11th grade students from the Mid-Atlantic United States. The students completed surveys in school in the spring of 2007 and 2008.

Gender differences in technology use were observed with girls texting, e-mailing/instant messaging, and working on the computer more than boys, and boys playing video games more than girls. Technology use also predicted later substance use for boys and girls. Importantly, technology use was observed to have both negative and positive effects on youth. Substance use also predicted later technology use for girls.

The sample only included adolescents from the Mid-Atlantic United States. In addition, the measures were based on self-reports. Nevertheless, results from this study highlight the importance of considering both negative and positive effects of technology on adolescents. Of note, social types of technology (texting and e-mailing) predicted more substance use for both boys and girls. As such, substance use prevention programs should target these types of technology.

Findings from this study underscore the importance of examining both directions of influence between technology use and adolescent adjustment.

We report an experiment conducted to gain insight into factors that may affect revenues in English auctions and lotteries, two commonly used charity fund-raising formats. In particular, we examine how changes in the marginal per capita return (MPCR) from the public component of bidding, and how changes in the distribution of values affect the revenue properties of each format. Although we observe some predicted comparative static effects, the dominant result is that lottery revenues uniformly exceed English auction revenues. The similarity of lottery and English auction bids across sales formats appears to drive the excess lottery revenues.

The field of industrial-organizational psychology has been unable to convince business executives that our science is able to effectively predict who will become valuable managers, let alone that our knowledge leads to measurable economic returns. The academic literature provides little guidance to a practitioner looking for guidance in regard to leadership development. We believe that leadership is complex and therefore requires a complex model to understand it and in turn aid leadership selection and development. We recommend focusing on defining specific leadership skills according to a leader's responsibilities and expected results or work outcomes to build taxonomy of leadership roles and work outcomes. To demonstrate the business case for engaging our field's services, we propose our field would be aided by adopting some concepts of a discipline widely accepted by executives, total quality management (TQM). An example of how TQM can be applied to leadership selection and development is presented.

Using data from a large cross-section of British establishments, we ask how different firm characteristics are associated with the predicted benefits to organizational performance from using team production. To compute the predicted benefits from using team production, we estimate structural models for financial performance, labor productivity, and product quality, treating the firm's choices of whether or not to use teams and whether or not to grant teams autonomy as endogenous. One of the main results is that many firm characteristics are associated with larger predicted benefits from teams to labor productivity and product quality but smaller predicted benefits to financial performance. For example, this is true for union recognition as measured by the number of recognized unions in an establishment. Similarly, when a particular firm characteristic is associated with lower benefits from teams to labor productivity or product quality, the same characteristic is frequently associated with higher predicted benefits to financial performance. This is true for the degree of financial participation and employee ownership and also for establishment size and a number of industries. These results highlight the advantages of analyzing broader measures of organizational performance that are more inclusive of the wide spectrum of benefits and costs associated with teams than the labor productivity measures frequently studied in the teams literature.

I study state dependence in social assistance receipt in Germany using annual survey data from the German Socio-Economic Panel for the years 1995–2011. There is considerable observed state dependence, with an average persistence rate in benefits of 68 per cent comparing to an average entry rate of just above 3 per cent. To identify a possible structural component, I estimate a series of dynamic random-effects probit models that control for observed and unobserved heterogeneity and endogeneity of initial conditions. I find evidence of substantial structural state dependence in benefit receipt. Estimates suggest that benefit receipt one year ago is associated with an increase in the likelihood of benefit receipt today by a factor of 3.4. This corresponds to an average partial effect of 13 percentage points. Average predicted entry and persistence rates and the absolute level of structural state dependence are higher in Eastern Germany than in Western Germany. I find only little evidence for time variation in state dependence around the years of the Hartz reforms.

Prior research finds that auditors can be distinctively committed to multiple workplace targets (e.g., their audit firm, supervisors, profession, and clients). This study investigates an underexamined target of auditor commitment – engagement teams. Given that these teams are responsible for performing key audit tasks for clients and external stakeholders, we argue that auditors' commitment to their team can affect auditor behavior. Using a sample of 121 auditors, our results indicate that quality social exchange relationships between individual auditors and their engagement teams, activated by perceptions of team fairness, and reciprocated with team commitment, are associated with beneficial group-oriented behavior. Specifically, we posit and find that perceived team fairness predicts perceived team support, perceived team support predicts team commitment, and team commitment predicts citizenship behavior directed toward the engagement team (e.g., helping the team by taking on extra responsibilities during an audit). We also find that the social exchange proxies of perceived team support and team commitment sequentially mediate the positive effect of perceived team fairness on team citizenship behavior, and that team commitment is a stronger predictor of team citizenship behavior than auditors' commitment to their firm.

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.