Predictive Analytics in Information Systems Research

This research essay highlights the need to integrate predictive analytics into information systems research and shows several concrete ways in which this goal can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. Extant IS literature relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models, and predictive power is assumed to follow automatically from the explanatory model. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show that predictive analytics and explanatory statistical modeling are fundamentally disparate, we show that they are different in each step of the modeling process. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure predictive model is best in terms of predictive power. We convert a well-known explanatory paper on TAM to a predictive context to illustrate these differences and show how predictive analytics can add theoretical and practical value to IS research

Could the ease of doing business be considered a predictor of countries' socio-economic wealth? An empirical analysis using pls-sem

The wealth of nations differs significantly due to different factors. One of the reasons identified by previous studies is the level of entrepreneurship promotion by governments. This aspect has scarcely been studied empirically to date. Therefore, this paper sheds some light on this regard through building a construct out of ten Ease of Doing Business Index (EDBI) measures developed by the World Bank and relating it with a construct shaped by two measures of socio-economic wealth (SEW), namely gross domestic product and the Human Development Index. To this end, we conduct a structural equation model analysis using partial least squares (PLS-SEM) method with a 2018 database comprising secondary data from 190 countries. As the main contribution of this study, the results show that good performance in the EDBI ranking predicts good performance in the SEW ranking. Additionally, this study is pioneer in the use of these rankings to build composite constructs (latent variables) and relate them. For these reasons, our findings are useful for both academia and governments responsible for promoting entrepreneurship, as this latter is identified as the key enabler of economic development

Predictive Non-equilibrium Social Science

Non-Equilibrium Social Science (NESS) emphasizes dynamical phenomena, for instance the way political movements emerge or competing organizations interact. This paper argues that predictive analysis is an essential element of NESS, occupying a central role in its scientific inquiry and representing a key activity of practitioners in domains such as economics, public policy, and national security. We begin by clarifying the distinction between models which are useful for prediction and the much more common explanatory models studied in the social sciences. We then investigate a challenging real-world predictive analysis case study, and find evidence that the poor performance of standard prediction methods does not indicate an absence of human predictability but instead reflects (1.) incorrect assumptions concerning the predictive utility of explanatory models, (2.) misunderstanding regarding which features of social dynamics actually possess predictive power, and (3.) practical difficulties exploiting predictive representations.Comment: arXiv admin note: substantial text overlap with arXiv:1212.680

Predictive Analytics in Forecasting

Predicting future demand can be of tremendous help to businesses in scheduling and allocating appropriate amounts of material and labor. The more accurate these predictions are, the more the business will save money by matching supply with demand as closely as possible. The approach for an accurate forecast, and the goal of this project, involves using data analytics techniques on past historical sales data. Working with Campus Dining, a year\u27s worth of their daily sales data will be analyzed and ultimately used for the end result of both an accurate forecasting technique and a way to display the results in a user friendly manner. The feasibility and effectiveness of doing so will be determined at the end of this project

How Does Positive Work-Related Stress Affect the Degree of Innovation Development?

Many studies sustain that work-related stress exerts pervasive consequences on the employees’ levels of performance, productivity, and wellbeing. However, it remains unclear whether certain levels of stress might lead to positive outcomes regarding employees’ innovativeness. Hence, this paper examines how the five dimensions of work-related stress impact on the employees’ levels of innovation performance. To this aim, this study focused on a sample of 1487 employees from six Italian companies. To test the research hypotheses under assessment, we relied on the use of the partial least squares (PLS) technique. Our results reveal that, in summary, the stressors job autonomy, job demands, and role ambiguity exert a positive and significant impact on the employees’ levels of innovativeness. However, this study failed to find evidence that the supervisors’ support–innovation and colleagues’ support–innovation links are not statistically significant. View Full-Tex

Is Gastronomy A Relevant Factor for Sustainable Tourism? An Empirical Analysis of Spain Country Brand

Tourism has become a fundamental industry for the economic growth of many countries. Due to this, there is growing competitiveness among the different destinations to attract as many tourists as possible. As a result, disciplines such as marketing have developed tools to differentiate some destinations from others and concepts such as place branding and country brand have emerged. One of the key factors forming the country brand is gastronomy, as food tourism is one way to reduce the growing problem of sustainability in tourism, as it impacts different aspects of the country’s environment. However, there is a great lack of scientific works that relate both variables. In this paper, we propose to establish that, in the case of Spain, tourists’ perception of Spanish gastronomy is a key element of its country brand. To do that, this study relies on the use of Partial Least Squares Structural Equations Modeling (PLS-SEM) using a 496 cases data set

Automating lead scoring with machine learning: An experimental study

Companies often gather a tremendous amount of data, such as browsing behavior, email activities and other contact data. This data can be the source of important competitive advantage by utilizing it in estimating a contact\u27s purchase probability using predictive analytics. The calculated purchase probability can then be used by companies to solve different business problems, such as optimizing their sales processes. The purpose of this article is to study how machine learning can be used to perform lead scoring as a special application case of making use of purchase probability. Historical behavioral data is used as training data for the classification algorithm, and purchase moments are used to limit the behavioral data for the contacts that have purchased a product in the past. Different ways of aggregating time-series data are tested to ensure that limiting the activities for buyers does not result in model bias. The results suggest that it is possible to estimate the purchase probability of leads using supervised learning algorithms, such as random forest, and that it is possible to obtain business insights from the results using visual analytic

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results