My journey of becoming a researcher started at a very young age, when I started to ask technical and philosophical questions, such as how things work, and why do they improve our civilization? Even some of my high-school teachers realized this potential, and prepared me for the National Physics Olympiad, where I became a team member. Next, my B.Sc. degree in electrical and electronics engineering helped me to understand how things work, from the sub-atomic, to the cosmic level. After my bachelor’s degree, I was lucky enough to find a managerial position at P&G, where I continued to learn how technologies affect work processes. In a few years, while strengthening my managerial career with an MBA, I realized that a master’s degree would not be enough to satisfy my thirst for knowledge. As a result, I applied for Ph.D. programs in information systems to pursue answering these questions at the academic level. Academic research has not only been a great source of stimulation, but also something that has helped me, and continues to help me acquire new skills. More importantly, it allows me to investigate philosophical questions at a scholarly-level. My goal, as a researcher, is to develop a rich and multifaceted understanding of how technology and behavioral factors drive organizational outcomes (e.g., business models, smart-service pricing decisions, and performance.) In a series of papers, I have developed insight into how technologies, such as cloud computing and internet of things, drive management strategies. As such, in my research, I provide frameworks to understand how technology artifact and IT services disrupt the conventional way of doing business. In other words, I am a behavioral modeler with an extensive training in statistics.

Specific methods I use range from empirical (e.g., multivariate probit model) to purely analytical (e.g., Hotelling’s location model). More importantly, modeling frameworks I develop could be applied to other disciplines. For example, my recent paper “Strategic Pricing of Horizontally Differentiated Services with Switching Costs: A Pricing Model for Cloud Computing” spans the intersection of information systems, operations management, and marketing disciplines.

Next, I present three main themes of my research portfolio and associated papers.

Theme 1: Information Technology as a Driver of Business Models and Strategies

The first theme concerns understanding how information technologies that generate big data (specifically, contemporary concepts such as cloud computing and internet of things) drive successful business models and are interrelated with strategies (such as pricing, or market presence.) Below, I discuss three papers that fit this research theme.

1. Strategic Pricing in Multi-Sided Markets: A Pricing Model for the IoT-Enabled Markets

How can firms price their products and services, as their ecosystems get smarter? In order to answer this question, this paper provides a stylized model and its expansion to characterize industries that got smarter and connected through the introduction of smart devices, a.k.a. the Internet of Things. First, we propose a basic model for a duopolistic multi-sided market with externality effects. Next, we expand this model to a case that considers cross-market network externalities. Our results reveal that, even if Internet of Things technologies facilitate complex multi-sided markets, there is a strategic pricing solution for firm profits. Moreover, a strategic firm can benefit from aforementioned cross-market externalities in terms of higher market share and equilibrium prices.

This study not only contributes to the theories of pricing information goods, but also provides a guideline for practitioners who make pricing and other strategic decisions for the Internet of Things goods and services.

2. Strategic Pricing of Horizontally Differentiated Services with Switching Costs: A Pricing Model for Cloud Computing

The major purpose of this study is to examine the cloud services pricing schemes and how they can improve previous pricing models by expanding the consumer set with time inconsistent behavior. The industry of cloud computing services is in its infancy, and firms employ pricing models based on conventional information goods. We offer a new approach to cloud services pricing considering the consumer discounting behavior. First, we propose a baseline model based on a profit maximizing duopolistic market serving to both rational and time inconsistent users. Our results reveal that the firms can profit from impatient users. In addition, we extend the baseline model with the effect of delayed network externalities because, by nature, information goods exhibit this property strongly. For the latter case, we show that the effect of network externalities reduces the impact of low switching costs and the monopolist benefits from time-inconsistent behavior. This study contributes to the theories of pricing information goods, and practitioners who make pricing decisions for cloud computing services.

Status: This study has appeared in International Journal of Electronic Commerce spring 2015 issue.

3. Exploring the Trade-Off between Immediate Gratification and Delayed Network Externalities in the Consumption of Information Goods

Our motivation for this paper originates from recent advances in consumption and payment technologies available for mobile commerce. Ubiquitous computing is enabling consumers worldwide to reach digital content and services whenever and wherever they request it. This capability further fuels “impatience” in consumption of such information goods. We model such consumer behavior using a hyperbolic discounting approach. A subset of these products, especially software, also inherits delayed network externalities as part of their consumption characteristics. This builds a tension between decision to consume now or to expedite consumption. We build a stylized model to assess the impact of immediate gratification on the profit maximizing behavior of a monopolist firm which produces an information good with network externalities. We find that serving ‘‘impatient’’ consumers is always profitable for a monopolist. For lower levels of network externalities, the monopolist can increase first period and decrease second period prices in equilibrium. As network externalities effect increases, prices converge to the traditional market (with exponential discounters) levels.

Status: This study is published in European Journal of Operational Research

4. Innovation via Business Process Design for the Internet of Things

There is an emerging market at the gate: the Internet of Things (IoT), which is expected to generate $14 trillion revenue in the next decade. The IoT refers to the equipping of all objects and people in the world with some form of identifying devices or sensors that can be networked together (such as refrigerators and thermostats using the internet). The main objective of this article is to examine opportunities and challenges of different business models in the IoT-enabled markets, and provide a basic roadmap to managers for sustainable growth.

Theme 2: Business Intelligence and Enterprise Systems

The second theme spans the business intelligence field with research in business analytics, data mining, learning systems, and ERP systems.

1. Strategic Alignment of Enterprise Systems and Business Strategies under Systems and Bivariate Approaches

Across holistic and bivariate approaches, we examine complex relationships amongst strategic alignment, strategic enterprise systems flexibility, and business performance. Our evidence is based on data collected from top management in North America. We observe a positive correlation between alignment and business performance. In addition, the relationship between enterprise systems strategic flexibility and performance have shown more significant results with a robust correlation when alignment act as a mediator. Our results indicate that while systems approach provides more significant results, bivariate approach allows deeper examination of constructs.

2. Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge

Electronic auction markets are economic information systems that facilitate transactions between buyers and sellers. Whereas auction design has traditionally been an analytic process that relies on theory-driven assumptions such as bidders’ rationality, bidders often exhibit unknown and variable behaviors. In this paper, we present a data-driven adaptive auction mechanism that capitalizes on key properties of electronic auction markets, such as the large transaction volume, access to information, and the ability to dynamically alter the mechanism’s design to acquire information about the benefits from different designs and adapt the auction mechanism online in response to actual bidders’ behaviors. Our auction mechanism does not require an explicit representation of bidder behavior to infer about design profitability—a key limitation of prior approaches when they address complex auction settings. Our adaptive mechanism can also incorporate prior general knowledge of bidder behavior to enhance the search for effective designs. The data-driven adaptation and the capacity to use prior knowledge render our mechanisms particularly useful when there is uncertainty regarding bidders’ behaviors or when bidders’ behaviors change over time. Extensive empirical evaluations demonstrate that the adaptive mechanism outperforms any single fixed mechanism design under a variety of settings, including when bidders’ strategies evolve in response to the seller’s adaptation; our mechanism’s performance is also more robust than that of alternatives when prior general information about bidders’ behaviors differs from the encountered behaviors.

Status: This study is published in Informs Journal on Computing

3. Multiple server investment decisions based on energy cost structure

This research forges IT investment decision with carbon emission based on the energy cost structure. We hypothesize an optimal server investment structure at different geographical locations based on energy cost structure and carbon emissions. IT investment decision cannot be made on fixed costs and this research includes carbon emission to the total cost of ownership of an IT system. In addition, electricity consumption and computing usage varies by the time of the day resulting in a time dependent total cost of ownership function. Therefore, allocating computing power over geographically distributed servers becomes a financially advantageous option.

Theme 3: A Behavioral Perspective of the Information Technology Role

This theme focuses on the role of collaborative information technologies to form an overarching theory for the role of information technologies.

1. Technology Role in Turbulent Teams: An Analytical Model and Empirical Validation

This study analyzes the impact of collaborative information technologies (CIT). We propose that the role of CIT can be more clearly understood by examining how it functions in the particular context of departure of an individual from a work team. Using an analytical model and laboratory experiments, we show that CIT serves to moderate the negative impact of a team member’s departure, and it can play an indirect but potentially significant role in enhancing group performance. We explain why and how team performance benefits from CIT when departure occurs. Moreover, we employ transactive memory theory to explain how individuals develop and exchange knowledge in a group and how skills and knowledge can be lost due to departure.

Status: This study is nominated for the best paper award at HICSS.

2. Gamification of Business Processes

I am a member of a research group formed at the University of Chicago on gamification. We have been developing an analytical model to understand digital goods. I treat this opportunity as a career builder.

Overall, we are interested in how game designers can make design decisions to learn about their players and maximize game revenues and other relevant designer objectives. This question is, of course, multi-faceted. Drawing on the talents of our multi-disciplinary team, we have identified several related questions which currently interest us. They concern the nature of player decision-making, the dynamics of game populations, economic principles of game design and monetization strategies, and how data can be used to support design decision-making.

Future Research Plan

I am committed to identifying problems with important implications for practice and to solving them with the utmost rigor. I will continue to conduct high quality research in the domains of business intelligence, enterprise systems, and technology management.

There are a couple other projects in my portfolio that will return long-term gains. First, we started a marketing big-data analytics project with Dr. Kerem Tomak (Head of shopping and attribution at Google and my previous advisor). After a couple meetings, we mutually agreed to suspend the project until I get more publications for tenure. Second, I am a member of a healthcare IS research group at UT Austin. It is a very large, geographically distributed research group working to address healthcare problems using IS tools such as data mining. This research group has the potential to produce steady publications. Finally, we have plans to expand my collaborative information technology role model with my brother (who is an Operations Management Professor at the Duke University). Any one of these future research directions has enormous potential to contribute to my research goal: to become an internationally recognized scholar.