ASU Scholarship Showcase

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over “swine flu,” as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.

Based on considerable neurophysiological evidence, Roy (2012) proposed the theory that localist representation is widely used in the brain, starting from the lowest levels of processing. Grandmother cells are a special case of localist representation. In this article, I present the theory that grandmother cells are also widely used in the brain. To support the proposed theory, I present neurophysiological evidence and an analysis of the concept of grandmother cells. Konorski (1967) first predicted the existence of grandmother cells (he called them “gnostic” neurons) - single neurons that respond to complex stimuli such as faces, hands, expressions, objects, and so on. The term “grandmother cell” was introduced by Jerry Lettvin in 1969 (Barlow, 1995).

This study is an attempt to use group information collected on climate change from farmers in eastern Uttar Pradesh, India to address a key question related to climate change policy: How to encourage farmers to adapt to climate change? First, we investigate farmers’ perception of and adaptation to climate change using content analysis and group information. The findings are then compared with climatic and agriculture information collected through secondary sources. Results suggest that though farmers are aware of long-term changes in climatic factors (temperature and rainfall, for example), they are unable to identify these changes as climate change. Farmers are also aware of risks generated by climate variability and extreme climatic events. However, farmers are not taking concrete steps in dealing with perceived climatic changes, although we find out that farmers are changing their agricultural and farming practices. These included changing sowing and harvesting timing, cultivation of crops of short duration varieties, inter-cropping, changing cropping pattern, investment in irrigation, and agroforestry. Note that these changes may be considered as passive response or adaptation strategies to climate change. Perhaps farmers are implicitly taking initiatives to adapt climate change. Finally, the paper suggests some policy interventions to scale up adaptation to climate change in Indian agriculture.

Buyers often make supplier selection decisions under conditions of uncertainty. Although the analytical aspects of supplier selection are well developed, the psychological aspects are less so. This article uses supply chain management and behavioral decision theories to propose that attributes of the purchasing situation (category difficulty, category importance, and contingent pay) affect cognition that, in turn, affects a supply manager's choice. We conducted a supplier selection behavioral experiment with practicing managers to test the model's hypotheses. When the context involves an important or difficult sourcing category, higher risk perceptions exist that increase preference for a supplier with more certain outcomes, even when that choice has a lower expected payoff. However, the presence of contingent pay decreases risk perceptions through higher perceived supplier control. We also find that a manager's risk propensity increases preferences for a supplier with less certain outcomes regardless of perceived risk. Our model and results provide a theoretical framework for further study into the cognitive aspects of supplier selection behavior and provide insight into biases that influence practicing supply chain managers.

This study examined the impact of three alternative types of goals (specific learning, general “do your best” learning, and specific performance) on team performance. Eighty-four-person teams engaged in an interdependent command and control simulation in which the team goal and task complexity were manipulated. Contrary to research at the individual level, teams with specific learning goals performed worse than did teams with general “do your best” learning goals or specific performance goals. The negative effects of specific learning goals relative to general “do your best” learning goals and specific performance goals were amplified under conditions of increased task complexity and were explained by the amount of coordination in the teams.

Purpose: The purpose of this paper is to review what we know - and don't know - about Generation Y's use of social media and to assess the implications for individuals, firms and society.

Design/Methodology/Approach: The paper distinguishes Generation Y from other cohorts in terms of systematic differences in values, preferences and behavior that are stable over time (as opposed to maturational or other differences). It describes their social media use and highlights evidence of intra-generational variance arising from environmental factors (including economic, cultural, technological and political/legal factors) and individual factors. Individual factors include stable factors (including socio-economic status, age and lifecycle stage) and dynamic, endogenous factors (including goals, emotions, and social norms). The paper discusses how Generation Y's use of social media influences individuals, firms and society. It develops managerial implications and a research agenda.

Findings: Prior research on the social media use of Generation Y raises more questions than it answers. It: focuses primarily on the USA and/or (at most) one other country, ignoring other regions with large and fast-growing Generation Y populations where social-media use and its determinants may differ significantly; tends to study students whose behaviors may change over their life cycle stages; relies on self-reports by different age groups to infer Generation Y's social media use; and does not examine the drivers and outcomes of social-media use. This paper's conceptual framework yields a detailed set of research questions.

Originality/Value: This paper provides a conceptual framework for considering the antecedents and consequences of Generation Y's social media usage. It identifies unanswered questions about Generation Y's use of social media, as well as practical insights for managers.

Information systems research is replete with examples of the importance of business processes defining IT adoption. Business processes are influenced by both organizational and operational concerns. We evaluate the comparative importance of operational and organizational influences for complementary IT systems. In the context of acute-care hospitals the analysis shows that an organizational approach to automating a process is related to different financial outcomes than an operational approach. Six complementary systems supporting a three-stage medication management process are studied: prescribing, dispensing, and administration. The analysis uses firm-level, panel data extracted from the HIMSS Analytics database spanning ten years of IT adoption for 140 hospitals. We have augmented the HIMSS dataset with matching demographic and financial details from the American Hospital Association and the Centers for Medicare and Medicaid Services. Using event sequence analysis we explore whether organizations are more likely to adopt organization boundary spanning systems and if the sequence of adoption follows the temporal ordering of the business process steps. The research also investigates if there is a relationship between the paths to IT adoption and financial performance. Comparison of the two measures suggests that the organizational model of adoption is observed more often in the data. Following the organizational model of adoption is associated with approximately $155 dollar increase in net income per patient day; whereas the operational model of adoption is associated with approximately $225 dollars decrease in net income per patient day. However, this effect diminishes with the adoption of each additional system thus demonstrating that the adoption path effects may only be relevant in the short-term.

With the advent of high-dimensional stored big data and streaming data, suddenly machine learning on a very large scale has become a critical need. Such machine learning should be extremely fast, should scale up easily with volume and dimension, should be able to learn from streaming data, should automatically perform dimension reduction for high-dimensional data, and should be deployable on hardware. Neural networks are well positioned to address these challenges of large scale machine learning. In this paper, we present a method that can effectively handle large scale, high-dimensional data. It is an online method that can be used for both streaming and large volumes of stored big data. It primarily uses Kohonen nets, although only a few selected neurons (nodes) from multiple Kohonen nets are actually retained in the end; we discard all Kohonen nets after training. We use Kohonen nets both for dimensionality reduction through feature selection and for building an ensemble of classifiers using single Kohonen neurons. The method is meant to exploit massive parallelism and should be easily deployable on hardware that implements Kohonen nets. Some initial computational results are presented.

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.

Increasing levels of financial inequality prompt questions about the relationship between income and well-being. Using a twins sample from the Survey of Midlife Development in the U. S. and controlling for personality as core self-evaluations (CSE), we found that men, but not women, had higher subjective financial well-being (SFWB) when they had higher incomes. This relationship was due to ‘unshared environmental’ factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men. Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship. Given the relatively small and male-specific relationship between income and SFWB, and the determination of both income and SFWB by personality, we propose that policy makers focus on malleable factors beyond merely income in order to increase SFWB, including financial education and building self-regulatory capacity.