Areas of Expertise

Data lifecycle management, Data visualization, Predictive analytics, Semiotics and database design, Cognitive aspects of data management, Data governance

Academic Degrees

• PhD, University of Arizona, 2002
• MBA, Management Studies, University of Bombay, 1994
• BE, Electronics, Malaviya Regional Engineering College, 1992

Professional Experience

• Professor of Operations and Decision Technologies, Kelley School of Business, 2018 – present
• Adjunct Faculty, Luddy School of Informatics, Computing and Engineering, 2017 – present
• Associate Dean for Information, Instructional Technologies and Academics, 2021 – 2022
• Co-Director, Kelley’s Institute for Business Analytics, 2011 – 2022
• Chairperson, Operation and Decision Technologies (ODT) Department, 2016 – 2021
• Associate Professor, Operation and Decision Technologies (ODT) Department, 2008 – 2017
• Assistant Professor, Operation and Decision Technologies (ODT) Department, 2002 – 2008

Awards, Honors & Certificates

• 2018, 2016, 2015, 2014 MBA Teaching Excellence Award, Kelley School of Business, Indiana University.
• 2014, 2012, 2010, 2006 Trustees’ Teaching Award, Kelley School of Business, Indiana University.
• 2011 Provost’s Award for Supporting Undergraduate Research and Creative Activity, Indiana University.
• 2011 Innovative Teaching Award, Kelley School of Business.
• 2008 Harry C. Sauvain Teaching Award, Kelley School of Business, Indiana University.
• 2007, 2006 Outstanding MSIS Faculty Award, Students’ Choice by the MSIS Class, Kelley School of Business, Indiana University.
• Best paper award nomination at Decision Sciences Journal: “Usability of Online Services: The Role of Technology Readiness and Context,” 2007.
• Best paper award nomination at the Hawaii International Conference on System Sciences (HICSS): Collaboration Systems and Technology track, 2008; Collaboration Systems and Technology track, 2005.
• Nominated for “Outstanding Junior Faculty Award” at Indiana University, 2005 and 2006. The award is sponsored by the Office of Academic Affairs and the Dean of the Faculties as well as the Office of Research and the University Graduate School.
• SBC Fellow, Indiana University, 2003.
• 2000 Jim L. LaSalle Award for Teaching Excellence by a Graduate Student Instructor, Department of Management Information Systems, University of Arizona.

Selected Publications

• Khatri, V., and Samuel, B. (2019). The current and future use of various analytics applications for managerial work: Trends in four business functions. Communications of the ACM, 62(4), 100-108.

  Abstract

  Abstract

  A 2014 IDC report predicted that by 2020, the digital universe—the data we create and copy annually—will reach 44 zettabytes, or 44 trillion gigabytes. With the explosive growth in organizational data, there is increasing emphasis on analytics that can be used to uncover the “hidden potential” of data. A 2014 Society for Information Management survey found analytics/business intelligence to be #1 among the top 15 most significant IT investments in the prior five years. It is not surprising that business analytics is increasingly central to managerial decision making within business functions: finance, marketing, human resources, and operations. Our survey included almost 200 U.S.-based business managers to provide their current and future use of different types of analytics applications for supporting managerial work in four business functions within an organization.

• Khatri, V., Samuel, B., and Dennis, A. R. (2018). System 1 and System 2 cognition in the decision to adopt and use a new technology. Information & Management, 55(6), 709-724.

  Abstract

  Abstract

  Most models of technology adoption and use assume a rational decision maker engaged in thoughtful deliberate consideration of the new technology. However, recent research in psychology concludes that such deliberate, rational, conscious decision-making (termed System 2 cognition) has less influence on behavior than originally thought; nonconscious automatic cognition (termed System 1 cognition), which is often influenced by personality characteristics and pattern matching based on past experience, also plays a key role in most decisions. As users adopt and use new technologies time and time again, a set of general expectations about new technology adoption begins to emerge. A user’s personality combined with this pattern of positive and negative experiences creates System 1 heuristics that are triggered when a user faces a similar decision in the future. The focus of this paper is to examine the extent to which the predispositions produced by System 1 automatic cognition – both enabling and inhibiting – versus the deliberate technology assessment produced by System 2 cognition influence technology adoption and use. We found that enabling predispositions influences the formation of intentions to use a new technology, and both enabling and inhibiting predispositions influence an individual’s ultimate follow through in acting on his or her intentions and actually using new technologies. Our research suggests that concepts previously seen as “determinants” of technology adoption and use (e.g., performance expectancy, effort expectancy, social influence, and facilitating conditions) are not really determinants but rather are important partial mediators in a larger nomological network that includes both automatic System 1 cognition and deliberate System 2 cognition.

• Samuel, B., Khatri, V., and Venkataraman, R. (2018). Exploring the Effects of Extensional Versus Intentional Representations on Domain Understanding. MIS Quarterly, 42(4), 1187-1209.

  Abstract

  Abstract

  Cognitive research suggests that understanding the semantics, or the meaning, of representations involves both ascension from concrete concepts denoting specific observations (that is, extension) to abstract concepts denoting a number of observations (that is, intension), and vice versa. Consonantly, extant conceptual schemas can encode the semantics of a domain intensionally (e.g., ER diagram, UML class diagram) or extensionally (e.g., set diagram, UML object diagram). However, prior IS research has exclusively focused on intensional representations and the role they play in aiding domain understanding. In this research, we compare the interpretational fidelity of two types of representational encoding of cardinality constraints, an intensional schema using an ER diagram and its extensional analog using a set diagram. We employ cognitive science research to conceptualize that extensional representations will enable enhanced understanding as compared with intensional representations. Further, given that prior research suggests that the semantics of cardinality constraints remain challenging to understand, we focus on mandatory and optional cardinality constraints associated with relationships in these representations. Based on our laboratory experiments, we find that understanding with an extensional representation was (1) at least as good as that with an intensional representation for mandatory cardinality constraints and (2) significantly better for optional cardinality constraints. We also conducted an applicability check of our results via focus groups and found support for the perceived significance of extensional representations in practice. Overall, this research suggests that the tradition in IS research of exclusively focusing on intensional encoding of domain semantics should be reexamined.

• Khatri, V., Ram, S., Snodgrass, R. T., and Terenziani, P. (2014). Capturing Telic/Atelic Temporal Data Semantics: Generalizing Conventional Conceptual Models. IEEE Transactions on Knowledge and Data Engineering, 26(3), 528-548.

  Abstract

  Abstract

  Time provides context for all our experiences, cognition, and coordinated collective action.   Prior research in linguistics, artificial intelligence and temporal databases suggests the need to differentiate between temporal facts with goal-related semantics (i.e., telic) from those are intrinsically devoid of culmination (i.e., atelic).  To differentiate between telic and atelic data semantics in conceptual database design, we propose an annotation-based temporal conceptual model that generalizes the semantics of a conventional conceptual model.  Our temporal conceptual design approach involves: 1) capturing “what” semantics using a conventional conceptual model; 2) employing annotations to differentiate between telic and atelic data semantics that help capture “when” semantics; 3) specifying temporal constraints, specifically non-sequenced semantics, in the temporal data dictionary as metadata.  Our proposed approach provides a mechanism to represent telic/atelic temporal semantics using temporal annotations.  We also show how these semantics can be formally defined using constructs of the conventional conceptual model and axioms in first-order logic.  Via what we refer to as the “semantics of composition,” i.e., semantics implied by the interaction of annotations, we illustrate the logical consequences of representing telic/atelic data semantics during temporal conceptual design.

• Khatri, V., and Brown, C. V. (2010). Designing Data Governance. Communications of the ACM, 53(1), 148-152.

  Abstract

  Abstract

  Organizations are becoming increasingly serious about the notion of "data as an asset" as they face increasing pressure for reporting a "single version of the truth." In a 2006 survey of 359 North American organizations that had deployed business intelligence and analytic systems, a program for the governance of data was reported to be one of the five success "practices" for deriving business value from data assets. In light of the opportunities to leverage data assets as well ensure legislative compliance to mandates such as the Sarbanes-Oxley (SOX) Act and Basel II, data governance has also recently been given significant prominence in practitioners' conferences, such as TDWI (The Data Warehousing Institute) World Conference and DAMA (Data Management Association) International Symposium. The objective of this article is to provide an overall framework for data governance that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance approach, strategy and design. Designing data governance requires stepping back from day-to-day decision making and focusing on identifying the fundamental decisions that need to be made and who should be making them. Based on Weill and Ross, we also differentiate between governance and management as follows: • Governance refers to what decisions must be made to ensure effective management and use of IT (decision domains) and who makes the decisions (locus of accountability for decision-making). • Management involves making and implementing decisions. For example, governance includes establishing who in the organization holds decision rights for determining standards for data quality. Management involves determining the actual metrics employed for data quality. Here, we focus on the former. Corporate governance has been defined as a set of relationships between a company's management, its board, its shareholders and other stakeholders that provide a structure for determining organizational objectives and monitoring performance, thereby ensuring that corporate objectives are attained. Considering the synergy between macroeconomic and structural policies, corporate governance is a key element in not only improving economic efficiency and growth, but also enhancing corporate confidence. A framework for linking corporate and IT governance (see Figure 1) has been proposed by Weill and Ross. Unlike these authors, however, we differentiate between IT assets and information assets: IT assets refers to technologies (computers, communication and databases) that help support the automation of well-defined tasks, while information assets (or data) are defined as facts having value or potential value that are documented. Note that in the context of this article, we do not differentiate between data and information. Next, we use the Weill and Ross framework for IT governance as a starting point for our own framework for data governance. We then propose a set of five data decision domains, why they are important, and guidelines for what governance is needed for each decision domain. By operationalizing the locus of accountability of decision making (the "who") for each decision domain, we create a data governance matrix, which can be used by practitioners to design their data governance. The insights presented here have been informed by field research, and address an area that is of growing interest to the information systems (IS) research and practice community.

• Montoya, M., Massey, A. P., and Khatri, V. (2010). Connecting IT Service Operations to Service Marketing Practices: Trust in IT Service Providers. Journal of Management Information Systems, 26(4), 65-85.

  Abstract

  Abstract

  The importance of building relationships with customers and trust in the services provider is well documented in the marketing literature. Conceptually, we extend this logic to the context of internal information technology (IT) services operations through the notion of the service delivery chain. The purpose of the study is to examine how key service mechanisms in operational IT implementation are related to employee perceptions of actual system benefits and trust in the IT services provider. We report on a study with 380 employees of 14 bank affiliates that were recently acquired by a bank holding company. The focus of the study is on postimplementation trust rather than preimplementation or initial trust, and the service provider is viewed as the object of trust rather than the technology. Our findings suggest that training, trial, and social influence are key service mechanisms an IT services provider can use to stimulate trust in the IT services provider and the realization of system benefits.

• Massey, A. P., Khatri, V., and Montoya-Weiss, M. (2007). Usability of Online Services: The Role of Technology Readiness and Context. Decision Sciences, 38(2), 277-308.

  Abstract

  Abstract

  An important prerequisite for the success of any online service is ensuring that customers' experience—via the interface—satisfies both sensory and functional needs. Developing interfaces that are responsive to customers' needs requires a perspective on interface design as well as a deep understanding of the customers themselves. Drawing upon research in consumer behavior concerning consumer beliefs about technology, we deploy an alternative way to describe customers based on psychographic characteristics. Technology readiness (TR), a multidimensional psychographic construct, offers a way to segment online customers based upon underlying positive and negative technology beliefs. The core premise of this study is that the beliefs form the foundation for expectations of how things should work and how specific online service interfaces are evaluated by customers. At the same time, usability evaluations of specific online services might be contingent on contextual factors, specifically the type of site (hedonic vs. utilitarian) and access method (Web vs. wireless Web). The aspects of usability examined here are those incorporated into the usability metric and instrument based on the Microsoft Usability Guidelines (MUG). The results of an empirical study with 160 participants indicate that (i) TR customer segments vary in usability requirements and (ii) usability evaluations of specific online service interfaces are influenced by complex interactions among site type, access method, and TR segment membership. As organizations continue to expand their online service offerings, managers must recognize that the interface exists to serve the customers, so their design must be matched to market needs and TR.

• Khatri, V., Vessey, I., Ramesh, V., Clay, P., and Park, S. (2006). Understanding Conceptual Schemas: Exploring the Role of Application and IS Domain Knowledge. Information Systems Research, 17(1), 81-99.

  Abstract

  Abstract

  Although information systems (IS) problem solving involves knowledge of both the IS and application domains, little attention has been paid to the role of application domain knowledge. In this study, which is set in the context of conceptual modeling, we examine the effects of both IS and application domain knowledge on different types of schema understanding tasks: syntactic and semantic comprehension tasks and schema-based problem-solving tasks. Our thesis was that while IS domain knowledge is important in solving all such tasks, the role of application domain knowledge is contingent upon the type of understanding task under investigation. We use the theory of cognitive fit to establish theoretical differences in the role of application domain knowledge among the different types of schema understanding tasks. We hypothesize that application domain knowledge does not influence the solution of syntactic and semantic comprehension tasks for which cognitive fit exists, but does influence the solution of schema-based problem-solving tasks for which cognitive fit does not exist. To assess performance on different types of conceptual schema understanding tasks, we conducted a laboratory experiment in which participants with high- and low-IS domain knowledge responded to two equivalent conceptual schemas that represented high and low levels of application knowledge (familiar and unfamiliar application domains). As expected, we found that IS domain knowledge is important in the solution of all types of conceptual schema understanding tasks in both familiar and unfamiliar applications domains, and that the effect of application domain knowledge is contingent on task type. Our findings for the EER model were similar to those for the ER model. Given the differential effects of application domain knowledge on different types of tasks, this study highlights the importance of considering more than one application domain in designing future studies on conceptual modeling.

• Khatri, V., Ram, S., and Snodgrass, R. T. (2004). Augmenting a Conceptual Model with Geo-spatio-temporal Annotations. IEEE Transactions on Knowledge and Data Engineering, 16(11), 1324-1338. 

  Abstract

  Abstract

  While many real-world applications need to organize data based on space (e.g., geology, geomarketing, environmental modeling) and/or time (e.g., accounting, inventory management, personnel management), existing conventional conceptual models do not provide a straightforward mechanism to explicitly capture the associated spatial and temporal semantics. As a result, it is left to database designers to discover, design, and implement—on an ad hoc basis—the temporal and spatial concepts that they need. We propose an annotation-based approach that allows a database designer to focus first on nontemporal and nongeospatial aspects (i.e., "what”) of the application and, subsequently, augment the conceptual schema with geospatiotemporal annotations (i.e., "when” and "where”). Via annotations, we enable a supplementary level of abstraction that succinctly encapsulates the geospatiotemporal data semantics and naturally extends the semantics of a conventional conceptual model. An overarching assumption in conceptual modeling has always been that expressiveness and formality need to be balanced with simplicity. We posit that our formally defined annotation-based approach is not only expressive, but also straightforward to understand and implement.

Edited on February 2, 2023