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Research in Operations Management and Information Systems Interface

Subodha Kumar* Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122, USA, [email protected]

Vijay Mookerjee Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, Texas 75080, USA, [email protected]

Abhinav Shubham Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122, USA, [email protected]

O wing to its multidisciplinary nature, the operations management (OM) and information systems (IS) interface distin- guishes itself from the individually focused perspective of both fields. The number and depth of contributions in this

department can help both disciplines advance to better address important theoretical and practical challenges of the busi- ness world. In this paper, we study the characteristics of problems at the interface between OM and IS, and review past work that has been instrumental in setting the tone and direction of research at this interface. We extend our discussion to provide directions for future research at the OM and IS interface in the domains such as smart city management, healthcare, deep learning and artificial intelligence, fintech and blockchain, Internet of Things and Industry 4.0, and social media and digital platforms.

Key words: information systems; operations management; interface; future roadmap History: Received: June 2018; Accepted: October 2018 by Kalyan Singhal, after 1 revision.

1. Introduction

Operations management (OM) and information sys- tems (IS) have had a long history of academic part- nership, evidenced by the fact that many academic institutions support these functions within the same organization unit or department. These fields draw heavily from a common pool of methods in man- agement science (e.g., optimization, statistics, and game theory) as well as from other reference disci- plines such as economics, econometrics, sociology, and psychology (Sidorova et al. 2008). In the busi- ness world, many successful IS applications are operational in nature, with firms heavily depending on information technology (IT) to improve opera- tional efficiency (Bharadwaj et al. 2007). According to Gartner (2017), worldwide IT spending is pegged to reach over $3.7 trillion in 2018. The US federal government alone allocated $95 billion in the annual federal budget for IT spending for the fiscal year 2018, a 1.7% increase over the fiscal year 2017.1 Over 78% of this amount was classified as operational spending. A recent Gartner survey on IT products and ser-

vices highlights cloud-based Software-as-a-Service (SaaS) enterprise products that provide IT based

operations management solutions to infrastructure and operations firms (McLellan 2017). Computer Economics, an established market research firm, conducted another market survey of top firms in 2016. Out of the respondents surveyed, a very high percentage (65%) reported an organizational increase in IT spending on the Operations function (McLellan 2017). These indicators, together with the anticipated growth in these areas, signal the impending onus on the academic community to dedicate time and effort to explore the interface between these two functions. The OM and IS inter- face needs to be better understood to guide the future development of both these disciplines. In this study, we review the opportunities to enhance and strengthen the OM-IS interface and discuss current and emerging areas of research.

2. Intersecting Domains

Research at the interface of two disciplines can be tricky. Purists will ask: Where is the contribution? Is it in OM or is it in IS? Without explicit support, interesting research at an interface could struggle to gain acceptance. It helps the interface to thrive if the boundaries of the participating disciplines are

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clearly defined and secure. When boundaries are ill defined, identifying the interface becomes doubly challenging. With this in mind, we begin with a brief discussion of the core problems in the OM and IS areas. This affords a better understanding of what lies at the interface. The IS area studies problems where the use of IT

creates economic value for individuals and organiza- tions. A single study may emphasize a subset of the essential ingredients of IS research, namely, IT, eco- nomic value, and individuals/organizations. The first dimension (IT) often lies at the center of gravity of IS work. However, the IT is more interesting if it is peo- ple-centric with the potential to create economic value. Since the OM disciple addresses frequently occurring (or operating) problems faced by individu- als/organizations and many successful IT applica- tions in business are operational in nature, it is natural that IS and OM are increasingly becoming more dependent on one another to create value. From the academic perspective, and from avail-

able literature on survey of the respective fields, it is easy to draw parallels between the kinds of prob- lems being focused on and the methodologies being used in the OM and the IS fields. In the IS area, there has been an extensive debate to understand what the scope of the field should be. Researchers have argued from multiple viewpoints on the diverse nature of the IS field, both in terms of the diversity in reference disciplines (e.g., OM, account- ing, marketing, and finance) as well as plurality of methodologies used by IS researchers. Although the merits (or otherwise) of diversity on the IS field is a much wider topic for discussion, our view is that this diversity has greatly benefitted the research conducted at the OM and IS interface because of the support it lends to the wide variety of research areas and problems that lie at the interface. Sidor- ova et al. (2008) identify five main areas of research in IS: (i) IT and organizations, (ii) IS development, (iii) IT and individuals, (iv) IT and markets, and (v) IT and groups. The major themes of research as identified by the study especially in the areas of IT and organizations (supply chain management, knowledge management, ERP, etc.) and IT and mar- kets (e-marketplaces, customer service, etc.) map strongly onto core areas of research that belongs in the OM field. We next explore the areas of intersec- tions further. Figure 1 highlights the key areas and subareas that

constitute the OM and IS interface. Researchers can contribute to the OM and IS interface in two ways: (i) using IS to solve OM problems, and (ii) applying OM techniques to solve IS problems. In the next subsec- tion, we discuss domains/problems involving the use of IS to solve OM problems.

2.1. Using Information Systems to Solve Operations Management Problems Figure 1, shows three main domains where research- ers have been able to solve OM problems using infor- mation systems: (i) information sharing across the supply chain, (ii) healthcare, and (iii) omnichannel retailing and recommender systems. We next explore these domains in detail.

2.1.1. Information Sharing Across Supply Chains. Traditionally, IS has contributed to OM by supplying the information needed to make better operational decisions. The most iconic work here is in the use of information sharing across the levels of a supply chain (Cachon and Fisher 1997, 2000, Lee et al. 1997). In their seminal paper, Lee et al. (1997) discuss the bullwhip effect and the distortion of demand information as it travels upstream in the supply chain. Their work points to the potential gain in efficiency from sharing information across the various levels of the supply chain. Following their work, there are important contributions concerning the use of IS to share information across all the levels of the supply chain. Notably, Cachon and Fisher (2000) show that IS applications in inventory management result in cost savings through lead-time and batch size reductions. They further postulate that information sharing could lead to more pronounced benefits in environments where demand is uncertain. Gavirneni et al. (1999) demonstrate the benefit of information sharing to manufacturers. Clark and Hammond (1997) study the benefit of prominent supply chain IT applications: electronic data interchange (EDI) and vendor man- aged inventory (VMI). Better (e.g., more accurate, timely, etc.) information

can reduce inventory costs not just through superior information concerning demand and supply, but also through better inventory tracking (e.g., RFID item tag- ging). Delen et al. (2007) explore the ‘information visi- bility’ provided through the implementation of RFID technology in the supply chain. They discuss positive implications of the unparalleled information visibility and granularity on supply chain efficiency. Whitaker et al. (2007) present the results of a field study of the RFID deployments and reported increasing accep- tance of the technology and expectations of early returns from adopters. Hardgrave et al. (2013) con- duct an empirical study on the efficacy of RFID tech- nology on reducing retail store inventory record inaccuracy (IRI). They conclude that RFID brought varying levels of amelioration to the determinants of IRI and brought about significant levels of IRI reduc- tion in some categories of products. Demirezen et al. (2016b, 2018) study information

sharing across partner firms. Using a differential games approach, they dynamically optimize the value

of co-creation across partner firms in an information sharing paradigm. Ghoshal et al. (2018) use a duopolistic game-theoretic model to show that strate- gic data sharing across competitors may improve the profitability of both firms. More recently, information technologies using Internet of Things (IoT) devices have improved the efficiency of manufacturing and supply chain systems, and RFID has played a very big part in adoption of IoT across industries (Atzori et al. 2010). The advent of IoT and similar information tech- nology backed advances in production methods has advanced the idea of ‘Industry 4.0’ (Lasi et al. 2014). With the inclusion of complementing technologies, such as cloud manufacturing and additive

manufacturing, in the ecosystem, there is a huge area of yet untapped potential for both the industry and academia. We discuss (in section 3.5) the emerging research potential in Industry 4.0 and IoT domains.

2.1.2. Healthcare. There has been active work in the healthcare field from the OM and IS communities. The industry is undergoing a period of rapid techno- logical transition with the adoption of electronic health records (EHR) and the formation of health information exchanges (HIEs). Yaraghi et al. (2015) present an empirical model to understand adoption and use of HIEs by Healthcare Providers (HPs). Their research sheds light on important motivating factors

Opera�ons Management and Informa�on Systems Interface

Informa�on Systems Applica�ons in Opera�ons

Management

Informa�on Sharing Across Supply Chain

Healthcare

Omni-Channel Retailing and Recommender Systems

Opera�ons Management Methods Applied to Informa�on Systems

So�ware Project Management

Cyber Security

Outsourcing

Grid/Cloud Compu�ng and Telecommunica�ons

Web and Mobile Adver�sing

Content Delivery Networks

Figure 1 Research Landscape of Operations Management–Information Systems Interface [Color figure can be viewed at wileyonlinelibrary.com]

underlying the adoption of HIEs. Further, Demirezen et al. (2016a) explore varying levels of participation of heterogeneous HPs in HIEs. They use a game-theore- tic model to obtain the equilibrium decisions of an HIE and HPs and show that additional incentives are needed from government agencies for the long-term sustainability of HIEs. They also present useful insights for policy-makers to improve the participa- tion of HPs in HIEs: an important step toward improving the quality of healthcare delivery and reducing healthcare costs. There has been important work in other interesting

areas of healthcare IS. For example, Angst et al. (2010) study the diffusion of EMR technology across US hos- pitals due to the mimetic effects of social contagion. Bhargava and Mishra (2014) study the effect of adop- tion of EMR on the productivity of physicians. Their work presents interesting results that can help allay fears of productivity loss upon adoption of new tech- nology. There are multiple such examples of innova- tive research in the area. The University of Texas at Dallas’ Top 100 business school research ranking database shows that, in the last 10 years alone, the Production and Operations Management Journal has published more than 30 articles in the area of healthcare. Researchers have tackled a multitude of issues,

such as patient scheduling and wait times, care qual- ity (Chen et al. 2018, Youn et al. 2016b), readmission rates, care delivery methods, optimal provider selec- tion (Youn et al. 2016a), and sustainability (Rajapak- she et al. 2015). Many of these studies have intersected with IS field. As several new technological advances mature in the healthcare field, we can expect to see several new applications and research problems emerging in the field. For example, bioinfor- matics and medical image informatics have continued to develop steadily over the last decade in the indus- try (Guha and Kumar 2018). We expect to see similar studies of impact in the academic domain in the near future.

2.1.3. Omnichannel Retailing and Recommender Systems. Several applications in Omnichannel retail- ing have drawn the attention of the OM community. Researchers have risen to this challenge and con- tributed heavily in the area (Bell et al. 2018, Brynjolfs- son et al. 2009, Gallino and Moreno 2014, Gao and Su 2017, Janakiraman et al. 2013, Kumar et al. 2018a, Mehra et al. 2018). Over the past few years, interest- ing research has been conducted in this area repre- senting both the OM and IS disciplines. For example, Brynjolfsson et al. (2009) introduce a new set of meth- ods to study cross channel competition between retailers. Gallino and Moreno (2014) empirically examine the impact of an implementation of buy

online and pick up in store (BOPS) strategy. Gao and Su (2017) study the effects of BOPS strategy on store operations. Bell et al. (2018) study the impact of using a showroom by online first retailers on the operational efficiency of the retailer. Furthermore, Mehra et al. (2018) study various competitive strategies by offline retailers to counter showrooming and propose new methods. Finally, and most recently, Kumar et al. (2018a) use transactions-level data from one of the lar- gest omni-channel retailers in the United States to study the impact of store opening on both online and offline sales. As mobile channels and social media become an important part of omni-channel retailing, the above studies need to be re-examined to confirm and extend extant findings. We expect to see more such studies in the near future, and anticipate many of them to be at the interface of OM and IS. Another important research direction in the retail-

ing domain pertains to the use of recommender sys- tems to improve business outcomes. For example, Ghoshal et al. (2015, 2018) utilize game-theoretic models to study the impact of recommender sys- tems on the competition between personalizing and nonpersonalizing firms. They analyze how compet- ing firms can benefit from sharing data with one another. Furthermore, Demirezen and Kumar (2016) present a mixed-integer-programming (MIP) formu- lation to optimize recommender systems for sub- scription-based firms. Interestingly, they consider and analyze the impact of inventory on online recommendations provided to consumers. The emergence of online movie/music streaming and the increasing use of online recommendations in this area, provide several future research opportuni- ties at the interface of OM and IS that could build upon the above studies. Essentially most of the problems studied in opera-

tions management occur frequently. This suggests that information can play a crucial role in maintaining and updating state information pertaining to resource levels, as well as pertaining to demand and supply information.

2.2. Applying Operations Management Techniques to Solve Information Systems Problems In the reverse direction, the OM field offers many use- ful methods to solve operating problems in the IS area. These problems have traditionally included soft- ware project management and the development and maintenance of software systems (Feng et al. 2006, Kulkarni et al. 2009, Tan and Mookerjee 2005). More recent works include operational issues in cybersecu- rity, information systems outsourcing (Ji et al. 2016), grid computing and telecommunications, web and mobile advertising and content delivery networks.

2.2.1. Software Project Management. Ji et al. (2005) propose an optimal control model to optimize the frequency of testing and integration activities during software development. Feng et al. (2006) pre- sent a model to optimize when to start maintenance in a development lifecycle as well as determine how long the maintenance ought to last. Kulkarni et al. (2009) develop a queuing model to optimize the allocation of resources to software maintenance effort. In another (but related) direction, Dawande et al.

(2008a) use an MIP formulation to address the prob- lem of pair formation and rotation during the course of a software project being developed using pair pro- gramming techniques. They also propose a heuristic based on Genetic Algorithms. These ideas were enhanced in Dawande et al. (2008b) to propose and study the so-called “Maximum Commonality Prob- lem,” that attempts to find an optimal module to pro- grammer-pair assignment to complete a project within a time constraint while maximizing the com- monality across programmer-pairs to reduce the effort spent on integration tasks. They use a network- flow-based approach in their analysis. Ji et al. (2011) propose an optimal control model to dynamically optimize lifetime value and enhancement of software products. Further, Fan et al. (2009) study short and long-term competition between shrink-wrap software providers and SaaS providers. As large software firms like Microsoft move more toward cloud-based soft- ware development and cloud-based applications,2

several novel research problems could emerge from the above studies.

2.2.2. Cybersecurity. The work on software sys- tem maintenance has spawned a more specialized stream of work on the management of cybersecurity systems. While traditional software systems mainte- nance occurs because their quality naturally degrades over time, the maintenance of cybersecurity systems occurs to guard against new attack strategies initiated by hackers whose attack rate often depends on the current quality of the security system (Mookerjee et al. 2011). More recently, ideas from studies of per- ishable inventory management have inspired models of managed monitoring services in cybersecurity (Ji et al. 2016). The basic idea in security monitoring is that processes inside the perimeter of a firm are moni- tored in real-time so that possible attacks can be detected and diffused before they can do much dam- age. However, since security-monitoring resources are limited, it is necessary to divert limited resources to processes that are most likely to do damage. The link to perishable inventory is that as time passes and a process does not show any symptoms of being mali- cious, it is less likely to be a source of attacks. This is

similar to the decay in time in the value of a perish- able inventory item. As the cybersecurity function matures in organiza-

tional settings, it will give rise to a host of managerial challenges that will require academic attention. There has been considerable work done to light the path for future academics interested in this domain. Notably, Kannan and Telang (2005) explore the issue of vulnera- bility disclosure in unregulated market settings and contrast it with a setting where a regulated infomediary acts as a disclosing entity. They find that market-based approaches to vulnerability disclosure underperforms in most cases with respect to maximizing social welfare when compared to infomediary-based approaches. Gal-Or and Ghose (2005) explore security information sharing in federally supported information sharing and analysis centers (ISACs) and conclude that information sharing yields greater benefits in more competitive industries. Arora et al. (2008) develop a framework to analyze the optimal vulnerability disclosure policy by a “social planner” with respect to the time of disclosure after a threat is initially identified. More recently, Sen et al. (2018) use the data from three different sources to investigate the impacts of (i) the perception of the vul- nerability discoverer about the software producer, (ii) the type of vulnerable software, and (iii) the severity of the vulnerability, on a vulnerability discoverer’s choice of disclosure timing.

2.2.3. Outsourcing. In the area of outsourcing, researchers have made efforts to model the impact that outsourcing in software development and man- agement can have on quality, especially with respect to customer satisfaction and project performance. Narayanan et al. (2010) study project outcomes of 182 unique outsourced projects from the perspective of the client. They conclude that the effect of out- sourcing on project performance is moderated by the following two factors: (a) the nature of the work, and (b) the maturity of the project. Similarly, Handley (2016) develop a theoretical model to examine the impact of misaligned governance and outsourcing capacity and capability of firm on the performance indicators of the outsourced project. The study also empirically validates theoretical results using a data- set containing project outcomes of 172 outsourced and 156 in-house projects. The recent geo-political volatility has had a very

strong impact on the market outlook for outsourcing. An interesting challenge for academics is to study the evolution of the outsourcing industry as it encounters these new challenges.

2.2.4. Grid/Cloud Computing and Telecommuni- cations. The emergence of cloud computing provides a fertile domain for the study of operational issues

(e.g., capacity planning, pricing, resource scheduling, real-time procurement of computing power, etc.) in the IS area (Buyya et al. 2002, Kumar et al. 2009). Online social media surfaces several challenges that have benefited from network optimization and dis- covery methods studied in OM. More recently, ideas of job-shop scheduling have been extended to address problems in the cloud-computing area. The cloud- computing problem is different from most traditional scheduling problems because in the cloud, the goal is often to minimize the cost of renting virtual machines to meet the computing needs of an organization. The cost of computing depends on the pricing structure offered by the cloud provider. The task scheduling problem for the cloud user and the pricing problem for the cloud provider are clearly related, since the provider needs to consider the behavior of the cloud user while choosing the optimal pricing structure. Most recently, Li and Kumar (2018) propose a game- theoretic model to study competitive offering strate- gies of service providers that offer cloud-based ser- vices. Another possible research direction in this domain

is studying the co-creation of data analytics tools/ methodologies and cloud computing. Dutta et al. (2017) and Kumar et al. (2017b) provide a detailed review of the applications of OM-based data analytics tools to solve problems in several related industrial domains, including the field of telecommunications. We believe that the operational and economic issues in grid/cloud computing and telecommunications are fruitful areas for researchers who wish to contribute at the OM-IS interface. Future studies in this domain may attempt to build on the above studies.

2.2.5. Web and Mobile Advertising. Internet- based advertising is another productive area of research at the OM-IS interface. Initial work in this area has been on static web advertising, mainly con- sidering banner ads (Dawande et al. 2003, 2005, Kumar et al. 2006). Kumar et al. (2007) extend these studies for the dynamic optimization of web advertis- ing based on real-time user response. Further, Fan et al. (2007) optimize strategies for providing digital media online with advertisements and/or subscrip- tion fees. In a similar direction, Kumar and Sethi (2009) present an optimal control theory model for finding pricing and advertising dynamically for web content providers. Further, Feige et al. (2008) propose a combinatorial allocation mechanism with penalties for banner advertising as a more efficient alternative to negotiation-based mechanisms that inflate costs and result in suboptimal allocation. Nazerzadeh et al. (2013) explore the idea of pay-per-action pricing regime for web advertising. They use a sampling- based learning algorithm to develop a mechanism

that is compatible with incentives. More recently, Mookerjee et al. (2012, 2017) optimize the decisions of an Internet advertising firm to show or not to show ads to a user. The problem they study is faced by an Internet advertising firm (Chitika) that operates in the Boston area. Chitika contracts with publishers to place relevant advertisements over a specified period on publisher websites. Using the prediction of the proba- bility of a click, they develop a decision model that uses a threshold to decide whether to show an adver- tisement to a visitor on a publisher’s website. An emerging domain in the web advertising area is

ad exchanges (Muthukrishnan 2009). Balseiro et al. (2014) propose a multiobjective stochastic control optimization problem to derive an efficient policy for online ad allocation in the presence of ad exchanges. Further, Balseiro et al. (2015) study the second price auction mechanisms used in ad exchanges and the competition it yields. They propose that in the pres- ence of budgets constraints, the dynamic interactions between advertisers are better approximated by a fluid mean-field equilibrium. More recently, Balseiro and Candogan (2017) study the contracting policy between advertisers and intermediaries who run cam- paigns on behalf of advertisers. They propose optimal contracting policy in a setting where advertisers’ bud- gets and targeting criteria are private. In this domain, the following two areas provide

promising directions for future research: (i) mobile advertising, and (ii) behavioral advertising. Mooker- jee et al. (2014) present a basic MIP model for optimiz- ing demand and supply in the mobile advertising domain. Future researchers may extend this study by incorporating more realistic and emerging issues in this model. In the mobile In-app advertising, Aseri et al. (2018) apply stochastic dynamic programming methods to study the procurement of impression opportunities in mobile ad exchanges. Kumar (2016) provides a nice summary of past and future trends of web and mobile advertising, and presents several possible future research directions in this domain. In the domain of behavioral advertising, a recent study by Kumar et al. (2018d) proposes an optimal insertion policy considering real-time user behavior. Since this is the first such study in this domain, there are several possible extensions. This domain would become even more interesting and promising for researchers with the emergence and growth of augmented/virtual/ mixed reality devices that can capture user behavior more accurately in real time.

2.2.6. Content Delivery Networks. Rising online traffic volume has led to several operational issues for content providers, such as increased latency and ser- ver outages because of demand surges. This has prompted the adoption of content delivery networks

(CDN) that optimally cache and deliver content based on the regional demand. They are typically geograph- ically collocated with Internet service providers (Hosanagar et al. 2008). Some of the big players in this area are Akamai, Limelight Networks, CDnet, etc. Hosanagar et al. (2008) derive optimal pricing strat-

egy when the content provider’s traffic is Poisson dis- tributed. Johar et al. (2012) extend this study by analyzing content provision in the presence of content piracy. Their study sheds light on how a publisher could leverage piracy to increase profits, even though the pirate essentially encroaches on the demand for the publisher’s content. More recently, Chiang and Jhang-Li (2014) study the competition equilibriums in the content delivery and media streaming markets. They identify conditions under which a content pro- vider should choose an incumbent CDN over an Internet backbone provider (IBP) that has recently diversified into content delivery provision. The increasing growth of CDNs provides interesting future research directions at the interface of OM and IS.

3. The Path Forward

Beyond supply chain management, IS also plays an important role in service operations. These include traditional roles such as web-based systems for cus- tomer care provision, as well as more advanced roles using artificial intelligence (A.I.) methods (e.g., pro- viding a natural language interface for service provi- sion) and knowledge-based Systems (e.g., providing access to past customer service solutions) (Bensous- san et al. 2009, Setia and Patel 2013). There are a num- ber of new possible advanced applications of IS and IT tools to solve OM problems. We identify the fol- lowing key areas where the interplay of OM and IS functions is revolutionizing industries and organiza- tions (as also referenced in Figure 2): (i) smart city management, (ii) healthcare management, (iii) deep learning and artificial intelligence, (iv) fintech and blockchain, (v) Industry 4.0 and Internet of things, and (vi) social media and digital platforms. We expect to see strong contributions pertaining to these areas in the future. We now explore them in further detail.

Figure 2 The Path Forward [Color figure can be viewed at wileyonlinelibrary.com]

3.1. Smart City Management With recent advancements in analytics and big data, ‘Smart cities’ concept has grown from just being a buzzword to realizable goals. Smart Cities are com- posed of network connected and technologically enabled urban surroundings that can be easily moni- tored and managed to drastically improve the quality of life for the city’s residents (Guha and Kumar 2018). This data-driven approach to city and infrastructure management can be easily complemented with research in areas such as performance monitoring and process optimization. One example of work in a related area is the paper

by Kahlen et al. (2018). They explore the feasibility of a virtual power plant (VPP) system composed of elec- tric vehicles (EVs). A VPP is a cloud-based system that aggregates power from distributed energy resources such as small biogas plants and rooftop solar systems. It can then distribute power as per demand through the grid to other grid connected con- sumers. Kahlen et al. (2018) examine the possibility of using parked electric vehicles as a source of energy for such a VPP. They develop a mixed rental-trading strategy based on the decision of when an EV should be made available as a rental and when should it be discharging to the grid. We expect to see similar topics and solutions emanating from this research stream in the near future.

3.2. Healthcare Management IS can make a profound impact on the operational problems in the healthcare domain. As discussed in section 2.1.2, new engaging challenges and opportuni- ties are encouraging researchers to take fresh approaches towards tackling new diverse set of prob- lems. Recent work in the area that may be influential in shaping the research narrative in the near future include the examination of the impact of length of stay (LoS) on the readmission rate by Oh et al. (2018). They also study the possible role of healthcare infor- mation technology (HIT) in reducing the readmission rate. Another interesting area for exploration in the healthcare management pertains to the operational problems associated with HIEs. As discussed earlier in section 2.1.2, some recent studies have analyzed the sustainability issues in HIEs and the adoption of HIEs. More recently, Janakiraman et al. (2017) exam- ine the impact of HIE participation and the doctor’s experience with the HIE on the treatment outcome, measured in terms of the 30-day readmission rate and the LoS. We expect to see similar topics being tackled in the near future. Finally, the recent emergence of online portals and

social media in healthcare would lead to several inter- esting problems at the interface of OM and IS. For example, a recent study by Khurana et al. (2018) find

that the introduction of doctors’ responses on an online healthcare portal has a significant causal impact on demand-side user perception of medical services offered. They demonstrate that, due to infor- mation asymmetry in healthcare, doctors use thought- ful online responses not only to interact socially with patients, but also to signal their expertise. Even though social media and online portals are becoming an integral part of healthcare management, there has been scant attention on analyzing the related opera- tional issues. Hence, this is an interesting and useful research domain for future researchers, especially for those working at the interface of OM and IS.

3.3. Deep Learning and Artificial Intelligence Another research area that is expected to grow in the future includes the application of new computational techniques – especially on data intensive problems – such as deep learning and Artificial Intelligence (AI) to solve problems in service and production opera- tions. In a recent study, Choi et al. (2018) present mul- tiple use cases and examples of applications of machine learning and AI techniques in OM research. The examples range from applications in forecasting (Baughman et al. 2016, Ferreira et al. 2016, Liu et al. 2016), inventory management (Huang and Van Mie- ghem 2014), detecting review manipulations (Kumar et al. 2018b), risk analysis, revenue management, and marketing and supply chain management. These examples and studies provide a good foundation and framework for future research in this domain.

3.4. Fintech and Blockchain According to Dan Schulman, C.E.O. of Paypal, over 2 billion individuals are without access to secure finan- cial services globally. In the U.S. alone, over 70 million individuals are not covered by institutional financial systems (Schulman and Kirkland 2017). This situation has necessitated disruptions in the financial industry using information technologies, commonly referred to as fintech. New technologies are helping re-envision a new financial ecosystem. Led by mobile digital pay- ment systems and distributed ledger technologies via blockchains, the fintech industry is in a period of rapid transition. Since the magnitude and direction of these technological disruptions is yet unclear, researchers need to carefully study the implications of these dis- ruptions and inform the wider industry on where and how these technological advancements could be har- nessed to optimally serve public interest. Some of the research problems in this context, that

need to be analyzed in the near future are as follows: (i) optimizing smart contracts in blockchains, (ii) designing a scaled blockchain and figuring out how many computers are necessary to validate each trans- action, (iii) dividing up the validation of transactions

efficiently to accelerates the process without sacri- ficing security, and (iv) managing operations of cryp- tocurrency, social trading, etc.

3.5. Industry 4.0 and Internet of Things According to Feng and Shanthikumar (2018), the manufacturing industry is set to undergo a revolution enabled by adoption of path-breaking technological advancements in device miniaturization and network connectivity. This new ‘Internet-triggered’ revolution (Feng and Shanthikumar 2018) is the Industry 4.0 phe- nomenon as we know it. As discussed in section 2.1.1, the adoption of a number of complementary techno- logical ecosystems is the primary reason behind this revolution. Industry 4.0 includes cyber-physical sys- tems involving: (a) IoT, (b) cloud computing, and (c) cognitive computing. One of the most significant tech- nological directions is the IoT phenomenon. IoT enables organizations to invest heavily in asset perfor- mance management systems, giving researchers the opportunity to work with a significant amount of real-time data. According to a report from Business Insider, there will be over 75 billion IoT devices by the year 2020 (Danova 2013). We expect to see researchers applying machine learning techniques to generate managerial insights from such large quanti- ties of data (Branstetter et al. 2018). Industry 4.0 is driven by four key disruptions:3 (i)

the astonishing rise in data volumes, computational power, and connectivity; (ii) the emergence of analyt- ics and business-intelligence capabilities; (iii) new forms of human-machine interaction such as touch interfaces and augmented–reality systems; and (iv) improvements in transferring digital instructions to the physical world, such as advanced robotics and three-dimensional (3-D) printing. Each of these dis- ruptions provides unique opportunities for OM and IS researchers to solve emerging problems using ana- lytical, empirical, and behavioral methodologies. For example, the 3-D printing can affect manufacturing processes, product design, as well as existing models of revenue management. Similarly, drones can speed up product delivery that can lead to new models of inventory holding, logistics, and pricing. The emer- gence of self-driving cars also provides interesting research directions for both OM and IS researchers.

3.6. Social Media and Digital Platforms This is a relatively new field where OM tools can be applied to a variety of emerging problems. In this domain, Qiu and Kumar (2017) conduct a field experi- ment to understand the voluntary knowledge provi- sion and content contribution though a social media- based prediction market. Furthermore, Kumar et al. (2018b) present a hierarchical supervised learning approach for detecting deceptive or false reviews.

They use several machine learning techniques, such as support vector machines, K-nearest neighbors, and logistic regression, to build meta classifiers that have an increased accuracy in predicting deceptive reviews. Kumar et al. (2018c) also use the data from yelp.com to empirically investigate online manage- ment response strategies in a digital platform. Mallipeddi et al. (2018a) use the twitter data to

empirically examine the effect of social media tone on engagement. Mallipeddi et al. (2018b) extend this study to optimize selection and scheduling of influ- encers in social marketing. Kumar et al. (2017a) study a recent phenomenon of trademarking hashtags in social media. They empirically investigate the impact of trademarking hashtags on social media engage- ment. Finally, Barron et al. (2018) present an empiri- cal analysis to study the impact of continuous real- time mobile feedback in the workplace, which is an emerging phenomenon in performance reviews. Another emerging area in the related domain that

would require solving different operational problems is on-demand economy and shared economy, including the ride hailing platforms such as Uber and Lyft. Recently, there has been increasing interest in solving operational problems arising in this domain, e.g., see Banerjee et al. (2016), Cachon et al. (2017), Bai et al. (2018), Guda and Subramanian (2018), Guha et al. (2018), and Taylor (2018). All of these studies, as well as the above-mentioned studies, provide an excellent foundation for future applications of OM tools to the problems emerging in social media and digital plat- forms.

4. Closing Thoughts

The research in IS disciple mainly deals with the pro- vision and use of IT-enabled systems to create value for individuals, organizations, and societies. Histori- cally, many successful applications of IS have been operational in nature. This suggests that IS applica- tions must evolve with the developments in the OM field. For the academic community, this means that there are increasing opportunities for research that lie at the intersection of these two fields. Aside from the featured areas in section 3 for future

research, there are several other possible pathways that could elicit interest from academics in the near to long term future. Notably, Sun and Xu (2018) explore the implications of ratings in service provisions where the service provider determines or influences the effort level required from the client as well as their own input, Shamir and Shin (2018) study the informa- tion sharing paradigm in trade association settings, Wang et al. (2018) study the impact of IT on port per- formance from the theoretical lens of resource-based view and transaction cost economics, Granados et al.

(2018) empirically study the market impact of opaque third party channels, Elmaghraby et al. (2018) explore a yet untapped area of liquidation markets for IT equipment resellers, Ghasemkhani et al. (2018) develop an optimal contracting models for peer-to- peer digital content distributors, Li et al. (2018) study the optimal channel distribution strategy for enter- prise software, Mehra and Saha (2018) explore the tradeoffs in demand and cost in the practice of intro- ducing public betas by software developers, and Bradley et al. (2018) study the joint effect of RFID and EDI use on hospital performance. These bold new ideas augur well for the domain. In closing, we would like to note that research at

the OM-IS interface is not just an opportunity for indi- vidual researchers, rather it is something that needs to be pursued for enhancing the two fields in what they study and how they study it. The IS field would benefit from research at the interface since it would widen the methods and the problems that are studied by IS researchers, whereas the OM field would benefit from interface research as it would bring new challenges and situations where the ideas of opera- tional efficiency can be improved.

Notes

1https://www.whitehouse.gov/sites/whitehouse.gov/file s/omb/budget/fy2018/ap_16_it.pdf (last accessed on October 13, 2018). 2https://docs.microsoft.com/en-us/dotnet/standard/moder nize-with-azure-and-containers/modernize-existing-apps-to- cloud-optimized/what-about-cloud-native-applications (last accessed on October 13, 2018). 3https://www.mckinsey.com/business-functions/operations /our-insights/manufacturings-next-act (last accessed on October 13, 2018).

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