Management Information Systems

Permanent URI for this collectionhttps://hdl.handle.net/10679/7976

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Now showing 1 - 19 of 19

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40th anniversary editorial: Looking backwards to move forward in management research
(Elsevier, 2022-08) Robinson, S.; Muratbekova-Touron, M.; Linder, C.; Bouncken, R. B.; Fındıkoğlu, Melike Nur; Garbuio, M.; Hartner-Tiefenthaler, M.; Thanos, I. C.; Aharonson, B. S.; Strobl, A.; Zhang, H.; Erz, A.; von Wallpach, S.; Karapinar, P. B.; Diedrich, A.; Saint-Germes, E.; Cole, R.; Management Information Systems; FINDIKOĞLU, Melike Nur
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The contingent value of the dedicated alliance function
(Sage, 2019-05) Fındıkoğlu, Melike Nur; Lavie, D.; Management Information Systems; FINDIKOĞLU, Melike Nur
Scholars have underscored the merits of a dedicated alliance function that promotes standardization, formalization, and centralization of alliance management practices, but some recent research finds no support for the claim that a dedicated alliance function creates value. We contribute to this debate by offering a contingency approach. Specifically, we conjecture that the contribution of the dedicated alliance function to value creation in an alliance increases with general partnering experience that the firm has accumulated with its various partners but declines with partner-specific experience that the firm has accumulated in recurrent alliances with the same partner. Our analysis of more than 15,000 alliances involving US-based software firms supports these conjectures and identifies boundary conditions for this function's effects. We conclude that instituting an organizational infrastructure that is meant to enhance a firm's ability to leverage its experience with various partners can restrict its gains from experience with specific partners. Nevertheless, by appropriately leveraging its dedicated alliance function, the firm can manage conflicting routines and overcome the tradeoff between efficient use of alliance management practices and responsiveness to partners in its alliance portfolio.
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A dynamic multi-level iterative algorithm for clearing European electricity day-ahead markets: An application to the Turkish market
(Taylor & Francis, 2023-05-12) Büke, B.; Shahmanzari, Masoud; Tanrisever, F.; Management Information Systems; SAYIN, Mesut
Designing and clearing day-ahead electricity market auctions have recently received significant attention from academia and practice alike. Given the size and the complexity of day-ahead market auctions, clearing them within the time limits imposed by the market is a major practical concern. In this paper, we model all the practical details of the Turkish day-ahead electricity market and provide a new multi-level iterative heuristic to clear the market. We compare our results with a commercial solver using data provided by Energy Exchange Istanbul. Our heuristic achieves an average optimality gap less than 0.09%, with an average solution time of just 14 s; whereas the commercial solver takes, on average, 18 min (and in some cases up to three hours) to find the optimal solution. We also demonstrate that using our heuristic solution to warm-start the commercial solver further reduces the solution time by 25%, on average. Overall, our heuristic proves to be very efficient in clearing the Turkish day-ahead market. We also test the performance of our algorithm as the problem size grows.
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Dynamic price and lead time quotation strategies to match demand and supply in make-to-order manufacturing environments
(Springer, 2020) Gel, E. S.; Keskinocak, P.; Gözbaşı, Tuba Yılmaz; Entrepreneurship; GÖZBAŞI, Tuba
We summarize the literature on dynamic quotation strategies under which a seller provides price and/or lead time quotes to a potential buyer, who then either accepts or rejects the quote. The goal of quotation strategies is to better match supply and demand by influencing and possibly smoothing out the demand. We identify some common observations and findings in this domain, and suggest directions of future work.
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Economic models of sponsored content in wireless networks with uncertain demand
(Wiley, 2014-09-09) Andrews, M.; Özen, Ulaş; Reiman, M. I.; Wang, Q.; Management Information Systems; ÖZEN, Ulaş
This chapter evaluates an approach whereby the service provider can tap into an alternative source of revenue, originating from sales of advertisements or products and channelled by the content provider in the form of sponsorship of viewing. It presents a simple economic model in which service provider congestion costs, end user (EU) bandwidth costs, and the price that the content provider must pay for sponsoring content are all determined on a per-byte basis. A key feature of the model is that the content has uncertain demand. The chapter focuses on the relationship between the service provider and a single content provider. It also presents a numerical example to demonstrate how the optimization might work in practice. The chapter indicates how the results can be adapted for the case of EU quotas. In most current wireless data plans, the EUs pay a certain fee for a fixed quota of data.
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Economic models of sponsored content in wireless networks with uncertain demand
(IEEE, 2013) Andrews, M.; Özen, Ulaş; Reiman, M. I.; Wang, Q.; Management Information Systems; ÖZEN, Ulaş
The interaction of a content provider with end users on an infrastructure platform built and maintained by a service provider can be viewed as a two-sided market. Content sponsoring, i.e., charging the content provider instead of viewers for resources consumed in viewing the content, can benefit all parties involved. Without being charged directly or having it counted against their monthly data quotas, end users will view more content, allowing the content provider to generate more advertising revenue, extracted by the service provider to subsidize its investment and operation of the network infrastructure. However, realizing such gains requires a proper contractual relationship between the service provider and content provider. We consider the determination of this contract through a Stackelberg game. The service provider sets a pricing schedule for sponsoring and the content provider responds by deciding how much content to sponsor. We analyze the best strategies for the content provider and service provider in the event that the underlying demand for the content is uncertain. Two separate settings are defined. In the first, end users can be charged for non-sponsored views on a per-byte basis. In the second we extend the model to the more common case in which end users purchase data quotas on a periodic basis. Our main conclusion is that a coordinating contract can be designed that maximizes total system profit. Moreover, the additional profit due to sponsoring can be split between the content provider and service provider in an arbitrary manner.
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Heuristic methods for the capacitated stochastic lot-sizing problem under the static-dynamic uncertainty strategy
(Elsevier, 2019-09) Randa, A. C.; Doğru, M. K.; Iyigun, C.; Özen, Ulaş; Management Information Systems; ÖZEN, Ulaş
We consider a lot-sizing problem in a single-item single-stage production system facing non-stationary stochastic demand in a finite planning horizon. Motivated by common practice, the set-up times need to be determined and frozen once and for all at the beginning of the horizon while decisions on the exact lot sizes can be deferred until the setup epochs. This operating scheme is referred to as the static-dynamic uncertainty strategy in the literature. It has been shown that a modified base stock policy is optimal for a capacitated system with minimum lot size restrictions under the static-dynamic uncertainty strategy. However, the optimal policy parameters require an exhaustive search, for which the computational time grows exponentially in the number of periods in the planning horizon. In order to alleviate the computational burden for real-life size problems, we developed and tested seven different heuristics for computational efficiency and solution quality. Our extensive numerical experiments showed that average optimality gaps less than 0.1% and maximum optimality gaps below 4% can be attained in reasonable running times by using a combination of these heuristics.
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Human and machine: The impact of machine input on decision making under cognitive limitations
(Informs, 2023-03) Boyacı, T.; Canyakmaz, Caner; de Vericourt, F.; Business Administration; CANYAKMAZ, Caner
The rapid adoption of artificial intelligence (AI) technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision makers (DMs) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors, and the DM's cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, although its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial.
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Linking macro-level goals to micro-level routines: EHR-enabled transformation of primary care services
(Springer, 2016-12-01) Fındıkoğlu, Melike Nur; Watson-Manheim, M. B.; Management Information Systems; FINDIKOĞLU, Melike Nur
Information and communication technologies are known to be instrumental in the enhancement of healthcare management capabilities in developing countries. Turkey a developing country has undergone a major healthcare transformation marked by the redesign of primary care delivery and the implementation of a nation-wide Electronic Health Records (EHR) system. In this research, presenting Turkey's case, we investigate the consequences of EHR implementation in developing countries. We argue that to better understand the consequences, we need to link macro-level healthcare goals with micro-level system usage behaviors that actualize the macro-level goals or alternatively result in unintended negative health outcomes. We posit that this linkage is achieved through the meso-level structures, namely the EHR and the organizational context, in which it is embedded. Hence, we examine the EHR's role in this relationship. Our findings indicate that EHR usage both enables and constrains the achievement of clinicians' professional goals in the context of primary care delivery. Moreover, goal alignment between the government agency as the designer of the system and the clinicians influence the outcomes of the EHR-enabled transformation. When the healthcare goals are aligned, the system enables the clinicians to achieve their professional goals and their system usage behaviors converge, contributing to improvements in health outcomes. Contrarily, when the goals are misaligned, the system constrains goal achievement and the clinicians show divergent usage behaviors, including goal abandonment. In turn, goal abandonment may lead to negative consequences and even adversely affect the achievement of population-level healthcare goals in the long run.
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Managing disease containment measures during a pandemic
(Wiley, 2023-05) Shahmanzari, Masoud; Tanrisever, F.; Eryarsoy, E.; Şensoy, A.; Management Information Systems; SAYIN, Mesut
Throughout the current COVID-19 pandemic, governments have implemented a variety of containment measures, ranging from hoping for herd immunity (which is essentially no containment) to mandating complete lockdown. On the one hand, containment measures reduce lives lost by limiting the disease spread and controlling the load on the healthcare system. On the other hand, such measures slow down economic activity, leading to lost jobs, economic stall, and societal disturbances, such as protests, civil disobedience, and increases in domestic violence. Hence, determining the right set of containment measures is a key social, economic, and political decision for policymakers. In this paper, we provide a model for dynamically managing the level of disease containment measures over the course of a pandemic. We determine the timing and level of containment measures to minimize the impact of a pandemic on economic activity and lives lost, subject to healthcare capacity and stochastic disease evolution dynamics. On the basis of practical evidence, we examine two common classes of containment policies—dynamic and static—and we find that dynamic policies are particularly valuable when the rate of disease spread is low, recovery takes longer, and the healthcare capacity is limited. Our work reveals a fundamental relationship between the structure of Pareto-efficient containment measures (in terms of lives lost and economic activity) and key disease and economic parameters such as disease infection rate, recovery rate, and healthcare capacity. We also analyze the impact of virus mutation and vaccination on containment decisions.
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Models for government intervention during a pandemic
(Elsevier, 2023-01-01) Eryarsoy, E.; Shahmanzari, Masoud; Tanrisever, F.; Management Information Systems; SAYIN, Mesut
While intervention policies such as social distancing rules, lockdowns, and curfews may save lives during a pandemic, they impose substantial direct and indirect costs on societies. In this paper, we provide a mathematical model to assist governmental policymakers in managing the lost lives during a pandemic through controlling intervention levels. Our model is non-convex in decision variables, and we develop two heuristics to obtain fast and high-quality solutions. Our results indicate that when anticipated economic consequences are higher, healthcare overcapacity will emerge. When the projected economic costs of the pandemic are large and the illness severity is low, however, a no-intervention strategy may be preferable. As the severity of the infection rises, the cost of intervention climbs accordingly. The death toll also increases with the severity of both the economic consequences of interventions and the infection rate of the disease. Our models suggest earlier mitigation strategies that typically start before the saturation of the healthcare system when disease severity is high.
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Multi-plant manufacturing assortment planning in the presence of transshipments
(Elsevier, 2023-11-01) Çömez-Dolgan, Nagihan; Dağ, H.; Fescioglu-Unver, N.; Şen, A.; Management Information Systems; ÇÖMEZ DOLGAN, Nagihan
In this study, we consider the assortment planning problem of a manufacturing firm with multiple plants. Making a plant capable of producing a product is costly, therefore the firm cannot manufacture every product in every plant. In case a customer's order in a particular region is not available in the closest plant, another plant can ship the product using transshipment, but at an extra transportation cost. If a demanded product is not produced in any plant, substitution from first choice to a second choice is also considered, which can be either satisfied by the closest plant, or by transshipment. The problem is to jointly determine assortments in all plants such that total profit after assortment and transshipment costs is maximized. The resulting problem is complex as transshipments and substitutions are intertwined to affect assortment decisions. We show that the optimal assortments are nested, i.e., the assortment of a plant with a smaller market share is a subset of the assortment of a plant with a larger share. The common assortment of all locations is shown to be in the popular set (i.e., no leapfrogging in product popularities), and a sufficient condition on substitution rate is derived for each individual assortment to be in the popular set. We conduct an extensive numerical study to understand the effects of allowing transshipments on resulting assortments. Moreover, we introduce approximate assortment planning algorithms that benefit from the derived structural properties, which are shown to generate near-optimal assortments in a broad range of instances tested.
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A newsvendor problem with markup pricing in the presence of within-period price fluctuations
(Elsevier, 2022-08-16) Canyakmaz, Caner; Özekici, S.; Karaesmen, F.; Business Administration; CANYAKMAZ, Caner
We consider a single-item single-period joint inventory management and pricing problem of a retailer selling an item that has selling price uncertainties. Unlike most of the literature on the newsvendor problem, we assume that price-dependent demand arrives randomly according to a stochastic arrival process whose rate depends on the fluctuating market input price process. The retailer's problem is to choose the order quantity and a proportional price markup over the input price to maximize the expected profit. This setting is mostly encountered by retailers that trade in different currencies or have to purchase and convert commodities for seasonal sales. For this setting, we characterize both the optimal inventory and markup levels. We present monotonicity properties of the expected profit function with respect to each decision variable. We also show that more volatile input price processes lead to lower expected profits.
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Partnering for prosperity: Small IT vendor partnership formation and the establishment of partner pools
(Taylor & Francis, 2021-03-04) Fındıkoğlu, Melike Nur; Ranganathan, C.; Watson-Manheim, M. B.; Management Information Systems; FINDIKOĞLU, Melike Nur
Small IT vendors increasingly establish intra-industry collaborative arrangements with other technology providers. Despite the criticality of this strategy, there is little research that provides insights into partnership formation. Our study attempts to close this gap.Building on resource dependency theory (RDT) and resource-based view (RBV), we posit that, depending on external market and internal resource considerations, small IT vendors either supplement or complement their IT resources and capabilities via partnerships. When seeking to expand the scope of their resource portfolio by accessing dissimilar resources, vendors are engage in complementary partnerships (goal: improving the scope of IT resources). However, if they seek to expand the scale of their portfolio, they engage in supplementary partnerships (goal: extending the scale of IT resources). Using a qualitative approach, we examine the partnership formation practices of seven small IT firms. We propose a conceptual framework with five constructs that illustrate dynamics underlying these IT service partnerships, i.e., External market considerations, Internal resource configurations, Partner considerations, Partnership exploration, and Partnership development. We find variations in partnership practices depending on the supplementary or complementary nature of resources being sought. We also find small IT vendors form and manage partner pools to mitigate risks associated with partnerships.
Open Access
Price and quality decisions of a service provider under heterogeneous demand
(Boğaziçi Üniversitesi, 2019) Özener, Başak Altan; Atahan, Pelin; Economics; Sectoral Education and Professional Development; ÖZENER, Başak Altan; DEMİRCİLER, Pelin Atahan
A monopolist service provider's quality and price decisions are analyzed in a vertically differentiated market where customers demand different quantities of a service. We find that depending on the relative sizes of the market segments and the difference in the valuations of different customers, the service provider may find it optimal to either offer a non-discriminating service or a discriminating service serving only high-valuation customers. The service provider never finds it optimal to serve the market segments that have low-valuation for quality when the discrimination strategy is optimal. © 2019 Bogazici Universitesi. All rights reserved.
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Queueing systems with rationally inattentive customers
(Informs, 2023-01) Canyakmaz, Caner; Boyacı, T.; Business Administration; CANYAKMAZ, Caner
Problem definition: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible, while service parameters are known with certainty. In practice, however, people only have "partial information" on the service environment, in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly, as it requires time, attention, and cognitive capacity, which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium when customers allocate their attention to learn uncertain queue lengths and delineate the impact of service characteristics. We provide a complete spectrum of the impact of information costs on throughput and show numerically that throughput might be nonmonotone. This is also reflected in social welfare if the firm's profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: We identify service settings where service firms and social planners should be most cautious for customers' limited attention and translate our results to advisable strategies for information provision and service design. For example, we recommend firms to avoid partial hindrance of queue-length information when a low-demand service is not highly valued by customers. For a popular service that customers value reasonably highly, however, partial hindrance of information is particularly advisable. Academiclpractical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design.
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The role of sociodemographic factors during a pandemic outbreak: Aggravators and mitigators
(Sociological Demography Press, 2021) Shahmanzari, Masoud; Eryarsoy, E.; Management Information Systems; SAYIN, Mesut
Many macro-and micro-level factors affect the spread of an infectious disease. Among them are sociodemographic, socioeconomic, sociocultural, health care system infrastructure, use of alcohol or substances, level of life disruptions because of chronic illnesses. Because of accuracy and timeliness issues, officials are often forced to make one-size-fits-all decisions across all regions. This paper offers a framework to analyze and quantify the interrelationships between a wide set of sociodemographic factors and the transmission speed of the pandemic to facilitate custom-fitted regional containment measures. The purpose of this paper is to investigate the role of a comprehensive set of sociodemographic factors in the diffusion of COVID-19 analytically. Our findings suggest that diverse sets of sociodemographic factors drive the transmission during different stages of the pandemic. In specific, we show that variables such as gender, age groups, daily commuting distances, modes of employment, poverty and transportation means are found to be statistically significant in the transmission speed of COVID-19. Our results do not suggest a statistically significant relationship between transmission speed and migration-related variables. We also find that the importance levels for the statistically significant variables vary across different stages of the pandemic. Our results point out a variety of public policy insights and implications.
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Setting the right incentives for global planning and operations
(Elsevier, 2016-09-01) Norde, H.; Özen, Ulaş; Slikker, M.; Management Information Systems; ÖZEN, Ulaş
We study incentive issues seen in a firm performing global planning and manufacturing, and local demand management. The stochastic demands in local markets are best observed by the regional business units, and the firm relies on the business units' forecasts for planning of global manufacturing operations. We propose a class of performance evaluation schemes that induce the business units to reveal their private demand information truthfully by turning the business units' demand revelation game into a potential game with truth telling being a potential maximizer, an appealing refinement of Nash equilibrium. Moreover, these cooperative performance evaluation schemes satisfy several essential fairness notions. After analyzing the characteristics of several performance evaluation schemes in this class, we extend our analysis to include the impact of effort on demand.
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Tactical inventory planning at alcatel-lucent’s repair and exchange services
(Informs, 2015-05-01) Doğru, M. K.; Özen, Ulaş; Management Information Systems; ÖZEN, Ulaş
Alcatel-Lucent, a major telecommunications equipment manufacturer, provides equipment, solutions, and services to customers worldwide. Post-sales support services are a growing business segment for the company, and require significant investment in spare parts. The inventory investment constitutes a considerable portion of the total cost of providing post-sales support services for new customer contracts; it is also an important element in estimating new contract offerings and evaluating and redesigning business processes that affect inventories. Because of Alcatel-Lucent's two-echelon supply chain structure, inventory sizing for post-sales support services is a complicated task that spreadsheet applications cannot handle adequately. Thus, the company's repair and exchange services (RES) group needed a fast, user-friendly, and generic software solution with advanced analytic capabilities to support its business tendering and evaluation processes. To meet this requirement, we developed the tactical inventory planning tool, which uses stochastic modeling and optimization algorithms, for this purpose. Although the tool's primary function is to support the RES business tendering process, it has also been useful in other business analysis because of its generic nature.

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