When to Be Agile: Ratings and Version Updates in Mobile Apps

Lean and agile models of product development organize the flexible capacity to rapidly update individual products in response to customer feedback. Although agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, and the apps’ customer ratings. We develop a structural model estimating the dependence of product versioning on (a) market feedback in the form of customer ratings against (b) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when they are able to exploit scale economies from coordinating development over a portfolio of apps. Agile operations increase firm payoffs by margins of 20% to 80%, and interestingly, partial agility is often sufficient to capture the bulk of these returns. Finally, turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories’ average user satisfaction rates by as much as 22%). This paper was accepted by Jayashankar Swaminathan, operations management.

CAPTURING MATHEMATICAL AND HUMAN PERCEPTIONS OF SHAPE AND FORM THROUGH MACHINE LEARNING

Classifying shape and form is a core feature of Engineering Design and one that we do this instinctively on a daily basis. Matching similar components to then reduce unique component counts, determining whether a competitors design infringes on copyright and receiving market feedback on product styling are all examples where shape and form comes into play. However, shape and form can be perceived in different ways from purely mathematical (e.g. shape grammars) to wholly subjective (e.g. market feedback) and these perceptions may not entirely agree.This paper examines the mathematical and human perceptions of shape and form through a study of classifying shapes that have been interpolated between one another, and in doing so, highlights the disparity in perceptions. Following this, the paper demonstrates how the emergent field of Machine Learning can be applied to capture mathematical and human perceptions of shape and form resulting in a means to twin this feedback into product development.

A Maximum Entropy Model of Bounded Rational Decision-Making with Prior Beliefs and Market Feedback

Bounded rationality is an important consideration stemming from the fact that agents often have limits on their processing abilities, making the assumption of perfect rationality inapplicable to many real tasks. We propose an information-theoretic approach to the inference of agent decisions under Smithian competition. The model explicitly captures the boundedness of agents (limited in their information-processing capacity) as the cost of information acquisition for expanding their prior beliefs. The expansion is measured as the Kullblack–Leibler divergence between posterior decisions and prior beliefs. When information acquisition is free, the homo economicus agent is recovered, while in cases when information acquisition becomes costly, agents instead revert to their prior beliefs. The maximum entropy principle is used to infer least biased decisions based upon the notion of Smithian competition formalised within the Quantal Response Statistical Equilibrium framework. The incorporation of prior beliefs into such a framework allowed us to systematically explore the effects of prior beliefs on decision-making in the presence of market feedback, as well as importantly adding a temporal interpretation to the framework. We verified the proposed model using Australian housing market data, showing how the incorporation of prior knowledge alters the resulting agent decisions. Specifically, it allowed for the separation of past beliefs and utility maximisation behaviour of the agent as well as the analysis into the evolution of agent beliefs.

Architecting and Developing Big Data-Driven Innovation (DDI) in the Digital Economy

To revamp with new creative age characterized by ongoing digital transformation, more and more industries are capitalizing on digital innovation for their sustainable business growth. Drawing on a systematic literature review, thematic analysis, and using the theories of dynamic capabilities and market orientation, this research scrutinizes a systematic process for developing analytics-based data-driven innovation (DDI). Findings suggest a standardized seven-step process for DDI, including product conceptualization, data acquisition, data refinement, data storage and retrieval, distribution, presentation, and market feedback.

Exponential synchronization and stabilization of delayed feedback hyperchaotic financial system

In a real financial market, the delayed market feedback and the delayed effect of government macrocontrol are inevitable, and both bring mathematical difficulties in studying stabilization and synchronization of the hyperchaotic financial system. However, employing the Lyapunov function method, differential mean value theorem, and suitable bounded hypotheses and pulse control technology result in globally asymptotic stabilization and synchronization criteria. It is the first paper driving the stabilization and synchronization criteria under the assumptions of double delays. Finally, numerical examples illustrate the effectiveness of the proposed methods.

Usability and Acceptability of a Mobile App to Help Emerging Adults Address their Friends' Substance Use (Harbor): Quantitative Study

Background Technology-assisted intervention and prevention strategies present opportunities for substance use–related research with emerging adults (EAs) and their peers. Emerging adulthood is a developmentally distinct period in which individuals between the ages of 18 and 29 years undergo unique emotional, cultural, developmental, and biological changes as they transition into adulthood. Crowdsourcing, or gathering feedback from a large group within web-based communities, offers researchers a unique and cost-effective way to obtain large amounts of information in a short period. Objective This paper presents market feedback obtained via Amazon’s Mechanical Turk from EAs (N=458) on the acceptability and utility of brief intervention scripts for a smartphone app currently under development. The mobile app, Harbor, teaches friends of EAs with substance use problems effective and supportive strategies for helping their friend make changes in their substance use behavior. Methods We examined feedback on the wording of the intervention scripts and estimated the market size of EAs who may use this app. Furthermore, we calculated correlations between script ratings and measures of personal risky drinking (ie, Alcohol Use Disorder Identification Test) and the participants’ use of confrontational, enabling, or supportive behaviors with an existing friend. Results Approximately half of our sample (208/458, 45.4%) indicated that they had a close friend for whom they had concerns about their substance use, suggesting a potentially high demand for an app such as Harbor. Initial findings suggest that peers who engage in less enabling behaviors with friends who have a substance use problem exhibited lower risky drinking behaviors overall (r206=−0.501; P<.001). Concerning acceptability, 98.0% (449/458) of the sample rated the scripts’ dialogue as either somewhat, moderately, or extremely realistic (mean 3.92, SD 0.48) on 5-point Likert scale items. Finally, 95.4% (437/454) of respondents indicated that the scripts would be at least slightly helpful for training peers to help their friends with substance use issues. Finally, individuals who were better able to identify enabling language in enabling scripts self-reported fewer enabling behaviors toward their friend’s substance use (r206=−0.236; P=.001). Conclusions There exists a demonstrated level of desirability and acceptability among EAs for a mobile app such as Harbor. EAs who wish to engage in more supportive behaviors with their friends who engage in substance use and who are amenable to assisting their friends with sobriety likely would use and benefit from this app.

More is Less: Publicizing Information and Market Feedback

We study whether and how publicizing internal information affects the value of financial markets to the real economy. By publicizing corporate filings, the SEC’s EDGAR web platform reduces the cost of acquiring internal information for outsiders and so makes it relatively less attractive to gather external information. We find that the staggered introduction of EDGAR reduced the sensitivity of firm investment to prices, consistent with prices being less informative to managers due to the crowding out of external information gathering. This crowding out effect is stronger when outsiders’ incentives for gathering information are stronger and for firms that rely more on external information. Our findings suggest that policies designed to “level the playing field” by publicizing internal information can have significant unintended consequences by reducing the informativeness of prices for real decisions.