Welcome to our Fall 2021 Newsletter

About the Leir Research Institute

The Henry J. and Erna D. Leir Research Institute for Business, Technology, and Society (LRI) creates value by integrating research and education to support economic and policy impacts that foster sustainable economic development, addressing critical global challenges to corporate and business continuity and growth.

The LRI operates in support of the NJIT 2025 four pillars: Diversity, Sustainability, Recognition, and Transformation. In coordination with the NJIT 2025 strategic plan, the LRI seeks to 1) promote collaborative research, 2) foster innovation and entrepreneurship, and 3) promote partnerships.

The vision of Henry J. and Erna D. Leir Research Institute for Business, Technology, and Society is to become recognized for business research that inclusively and collaboratively engages our academic, corporate, governmental, and non-profit partners. The Leir Research Institute will be a perpetual legacy honoring the memories of Henry J and Erna D. Leir and will also support the NJIT Martin Tuchman School of Management as it integrates academic research with important societal needs to solve critical societal problems.

The Henry J. and Erna D. Leir Research Institute for Business, Technology, and Society's research builds upon and leverages decades of NJIT experience and intellectual capital in the fields of sustainability and industrial ecology, environmental science, operations management and decision analytics, organizational behavior, and business data science.

More About Us

NJIT Joins NSF Research Hub with Princeton, Rutgers, Delaware

New Jersey Institute of Technology today joined other area universities as part of a new regional research hub, funded by a $15 million National Science Foundation grant and led by Princeton University, to help faculty and students convert federally sponsored research into successful businesses.

Research Spotlights

Shanthi Gopalakrishnan

How does the Covid-19 pandemic impact Innovation in industries? Crisis and Innovation

We contribute to the crisis innovation theory and practice by suggesting that there are two categories of innovation during a multi-level crisis such as the pandemic: (1) reactive, threat-driven innovations that are created to contain and respond to the disruptions from the crisis; and (2) proactive, opportunity-driven innovations that are created to capitalize on environmental needs. We highlight the role of human and physical interdependence in organization’s core technologies that create threats for some organizations and opportunities for others during the pandemic.

Gopalakrishnan, S., & Kovoor-Misra, S., 2021. Understanding the impact of the Covid-19 pandemic through the lens of organizational innovation. Business Research Quarterly.  Accepted for publication, April, 2021

Does exposure to a crisis make organizations more resilient? 

Why do some organizations bounce-back from traumatic events more quickly than others? In this study, we build on the research on resilience and argue that organizational recovery from a traumatic event is informed by the perception of threat. Higher perception of threat increases inter-organizational collaboration and the care associated with the deployment of slack as well as to learning. We tested our arguments with a sample of US and non-US firms before and after the 9/11 terrorist attacks and found that, due to spatial proximity, US firms’ higher perception of threat led to a larger increase in the frequency of inter-organizational alliances than that of non-US firms. This preference was more frequently directed towards local partners and demonstrated a distinct emphasis on slack and learning. Contrary to conventional wisdom, our findings suggest that organizational resilience in the face of a traumatic event benefits not from immunity but from spatial proximity to the threat. Proximity increases the perception of threat, and with it, the impetus for adaptation. 

Mithani, M., Gopalakrishnan, S., Santoro, M.D., 2020. Does Exposure to a traumatic event make organizations more resilient? Accepted for Publication. Long Range Planning. August 2020.

How Identifying with the organization allows individuals to stay in control and stay grounded during a Crisis

Organizational identification could play an important role during crises, if it contributes to individuals’ perceptions of control. This study examines this relationship and unpacks some of its complexities by investigating the mediating role of job satisfaction and citizenship behaviors. Using the survey method, quantitative data was collected from 354 individuals from a nonprofit organization that filed for Chapter 11 bankruptcy. We found that: 1) job satisfaction fully mediates the relationship between organizational identification and perceived control; 2) the perceived severity of the crisis moderates the relationship between organizational identification and job satisfaction. Practically, this study shows that leaders can rely on individuals who identify with their organizations during a crisis, because they experience job satisfaction and a sense of control 

Kovoor-Misra, S., Gopalakrishnan, S., & Zhang, H., 2021. “Staying Grounded! Organizational Identification and Perceived Control During Crises. Journal of Change Management, Vol. 34, 2, 366-384

How can firms improve their R&D skills through strategic alliances?

This research examines the impact of coopetition (i.e., competitor alliances) on the development of internal R&D human capital. The study was conducted using survey data from 111 biotech firms in Spain and US. Results show a mediation relationship: coopetition increases a firm’s internal R&D human capital via its proactiveness to pursue R&D partnerships. To further examine the link between competitor alliances and R&D partnerships, we also investigate the role of two moderators, alliance satisfaction and alliance coordination. We argue that the two factors exert opposite moderation effects on the relationship between coopetition and proactiveness to pursue R&D partnerships. Results demonstrate that when a firm and its alliance partners are satisfied with each other, the effect of coopetition on proactiveness decreases, but the moderation effect of alliance coordination, though predicted to be in the opposite direction, is not significant. 

Vlaisavljevic, V., Gopalakrishnan, S., Cabello, C., Zhang, H., Guilbault, M., 2021. Dancing with wolves: How R&D capital can benefit from coopetition. Accepted for publication. R&D Management. Accepted April 2021. 

Jim Shi

Blockchain-empowered Newsvendor optimization

Technology has been widely adopted as a backbone for supply chain management, especially in the context of retailing. For example, Blockchain Technology (BCT) has been embraced as a disruptive technology, though it is still in a nascent stage. This study examines the technology adoption for Newsvendor model (a.k.a Newsboy), as exemplified by a blockchain system. It aims to shed light on how BCT adoption impacts the optimal ordering decisions and the corresponding optimal profit. Importantly, we investigate the optimal adoption of technology for profit optimization while considering the adoption cost. As a result, we visualize how technology adoption shifts the curve. For some selected demand types (e.g., Uniform and Normal distributions) and the Cobb-Douglas cost function, we derive closed-form expressions for the optimal decisions, based on which some useful insights have been developed. Although high adoption of BCT leads to higher demand and lower ordering cost, some counter-intuitive examples have been devised to reveal that the increase in adoption degree might lower the optimal order quantity and it is not always profitable to adopt a higher BCT even if there is no adoption cost. Finally, a sequence of numerical studies complements our analytical results with rich and useful insights.

Chang, A., M.N. Katehakis, J. Shi and Z. Yan “Blockchain-empowered Newsvendor optimization”, International Journal of Production Economics (IJPE), 238, 108144, 2021.

Blockchain Design for Supply Chain Management

Blockchain related research is still in its infancy, and is mostly focused on security and scalability. Very little of this research examines at its impact and design issues from management perspectives, especially from the perspective of Supply Chain Management (SCM). To investigate the impact of blockchain technology (BCT) on SCM and the inherent design issues, we consider a generic stochastic model, where a firm seeks to maximize the total expected discounted profit, by jointly managing (i) blockchain design,(ii) production and ordering decisions, and (iii) dynamic pricing and selling. We first show that the deployment of BCT can assist firms in reducing order quantities, lowering selling prices and reducing target-inventory levels. It is also shown that volatility of either supply or demand lowers the expected profit. The analysis is robust with some major extensions, such as lost-sales of demand and random capacity. Finally, our numerical study accumulates useful managerial insights. For example, subject to tech-savvy customer behavior, some types of goods (eg, credence goods and experience goods) greatly benefit from the adoption of BCT, but it may not prove beneficial to leverage BCT for certain others (eg, search goods). Considering the lifecycle of a typical good, it is recommended to adopt BCT as early as possible and to adopt it to a higher degree at an earlier stage.

Chang, J., Katehakis, M. N., Melamed, B., & Shi, J. J. (2021). Blockchain design for supply chain management. Available at SSRN 3295440. Working paper, NJIT.

Capacity reallocation via sinking high-quality resource in a hierarchical healthcare system

This paper studies the capacity reallocation in a hierarchical medical ecosystem via sinking high-quality resource from high-level hospitals to low-level hospitals. To facilitate the capacity sinking, we develop two payment schemes: fee-for-capacity (FFC) and performance payment (PP). Under the FFC scheme, the low-level hospital always pays a unit capacity sinking price to the high-level hospital, whereas under the PP scheme, the reallocation price is paid contingent on the increased patient visits at the low-level hospital due to capacity sinking. By considering the profit- and utility-maximizing behaviors of strategic parties, we build a four-stage Stackelberg sequential game model within a queuing framework to derive the equilibrium results in terms of the low-level hospital’s capacity, the high-level hospital’s capacity sinking rate, and the funder’s capacity sinking price. In the absence of funder’s coordination, it is shown that any increase in sinking price always reduces the capacity sinking rate. In the presence of funder’s coordination, we find that: (1) the payment schemes under study will not alter the efficiency or coordination of the overall healthcare systems; (2) for the setting with a high perceived value by patients, under each payment scheme, the capacity sinking program is efficient to increase the high-level hospital’s profit and the social welfare as well, but it lowers the patient visit rate at the low-level hospital; (3) for the setting with a higher difference between the patients’ perceived values at the two levels of hospitals, the capacity sinking program is efficient to increase the patient visit rate at the low-level hospital and the social welfare as well, but it sacrifices the high-level hospital’s profit. Finally, numerical studies provide more useful managerial insights.

Wang, J., Z. Li, A. Chang, and J. Shi, “Capacity Reallocation via Sinking High-Quality Resource in a Hierarchical Healthcare System”, Annals of Operations Research 300(1), 97-135, 2021.

Hospital referral and capacity strategies in the two-tier healthcare systems

Healthcare referral has been widely advocated and adopted through the implementation of the two-tier healthcare systems whereby patients are transferred from a comprehensive hospital provider (CHP) to a primary hospital provider (PHP). However, operationally, exactly how to implement the healthcare referral program remains a challenging research question, especially when considering the possibility of patient revisits and the coordination needed between the CHP and PHP. To address such a challenge, this paper considers the two-tier healthcare systems consisting of a CHP and a PHP. By establishing a three-stage Stackelberg game within a queuing framework among the CHP, the PHP, and their patients, we first investigate the equilibrium strategy in terms of the CHP's referral rate and the PHP's capacity level, and then examine the impact of revisit rates and referral payments (RP) on the healthcare system and the equilibrium outcomes (e.g., expected utility, social welfare, and waiting times). Two major findings of our study are: (1) both the equilibrium referral rate and the equilibrium capacity first increase and then decrease according to the revisit rate; in addition, the patient referral process always improves the PHP's performance but is likely to sacrifice the social welfare of the CHP. (2) There exists an RP threshold value such that if the RP is below the threshold, then all the permitted patients should be referred and the system performance will be enhanced, in which case a win-win situation in terms of expected utilities can be attained that benefits all the stakeholders, i.e., the CHP, the PHP, and the patients. Otherwise, only a portion of the permitted patients can be referred, and an increase in RP always reduces the efficiency of the healthcare delivery system, i.e., a higher RP mitigates the operational performance of the healthcare system. Our analysis sheds light on how to implement a healthcare referralscheme.

Wang, J., Z. Li, J. Shi, A. Chang, “Hospital Referral and Capacity Strategies in Two-Tier Healthcare Systems”, Omega, The International Journal of Management Science, 100, 102229, 2021.

Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery duration

Abstract. Operation room (OR) management has been among the mainstream of hospital management research, as ORs are commonly considered as one of the most expensive resources. The complicated connection and interplay between ORs and their upstream and downstream units has recently attracted research attention to focus more on allocating medical resources efficiently for the sake of a balanced coordination. As a critical step, surgical scheduling in the presence of uncertain surgery durations is pivotal but rather challenging since a patient cannot be hospitalized if a recovery bed will not be available to accommodate the admission. To tackle the challenge, we propose an overflow strategy that allows patients to be assigned to an undesignated department if the designated one is full. It has been proved that overflow strategy can successfully alleviate the imbalance of capacity utilization. However, some studies indicate that implementation of the overflow strategy increases the readmission rate as well as the length of stay (LOS). To rigorously examine the overflow strategy and explore its optimal solution, we thus propose a Fuzzy model for surgical scheduling by explicitly considering downstream shortage, as well as the uncertainty of surgery duration and patient LOS. To solve the fuzzy model, a hybrid algorithm (so-called GA-P) is developed, stemming from Genetic Algorithm (GA). Extensive numerical results demonstrate the plausible efficiency of the GA-P algorithm, especially for large-scale scheduling problems (e.g., comprehensive hospitals). Additionally, it is shown that the overflow cost plays a critical role in determining the efficiency of the overflow strategy; viz., benefits from the overflow strategy can be reduced as the overflow cost increases, and eventually almost vanishes when the cost becomes sufficiently large. Finally, the Fuzzy model is tested to be effective in terms of simplicity and reliability, yet without cannibalizing the patient admission rate.

Wang, J., Z. Dai and J. Shi, “Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations.” Forthcoming, 2022, Annals of Operations Research (ANOR).

Optimal Selling Policies for Farmer Cooperatives

Sustainability and long-term prosperity are chronic challenges in the agriculture sector of many countries. To address such challenges, farmer cooperatives are formed as an innovative approach to improve the livelihoods of millions of farmers around the world. Inspired by real-life practice in the Kenya coffee industry, we study a class of stochastic and dynamic inventory models for storable agricultural products with random exogenous supply and price. For a variety of cost functions relevant in practice, we characterize the optimal selling policies to maximize the farmer cooperatives’ expected profit. We show that for concave inventory holding cost, the sell-all-or-retain-all (r, R) (or sell-all-or-retain-all R) policies are optimal with (without) the fixed selling cost; for convex holding cost, the sell-down-to (S, s) (or sell-down-to s) policies are optimal with (without) the fixed selling cost. For the special case of linear holding cost, the optimal policy is a cut-off price policy and we derive closed-form expressions for the optimal policy and the optimal total discounted profit. We discuss the model extensions to include general stochastic harvest and price processes, selling/storage capacity limits, price-dependent random demand with a spot market, and the flexibility of procurement from other producers, and then perform a numerical study to quantify the impact of the optimal solutions. Reconciling the theory with practice, useful insights and guidelines are provided to help farmer cooperatives make strategic selling decisions.

Shi, J., Y. Zhao, K. Kiwanuka, and A. Chang, “Optimal Selling Policies for Farmer Cooperatives”, Production and Operations Management, (POMS), 28(12), 3060-3080, 2019. 

*Business Week’s top 20 Premier Journal; Financial Times Research’s 45 Premier Journal, UT Dallas Listed top 24 journals.

Stochastic Sequential Allocation for Creative Crowdsourcing

Creative crowdsourcing is an innovative online business model in which a platform marshals independent professionals, such as designers, to conduct projects of creative work. Typically, clients submit project requests stochastically to a platform which has a pool of registered creators (e.g. designers). For each project, a designer independently decides whether to participate and submit a design, and the client chooses one winner from all submissions, or rejects all, based on some subjective discretion. In practice, most platforms rein the process by specifying a maximum number of designers working on a particular project. The inherent allocation decision is critical to its profitability and success, but it is challenging due to the uncertainty pertaining to both the supply (i.e., designer’s participation) and the demand (i.e., project’s arrival and value). To tackle the problem, we devise a stochastic sequential allocation model and analyze the properties of the optimal control policy, whereby we develop a computationally efficient algorithm. Our study reveals that the optimal policy follows an inverted-U-shaped function of the project value, implying a higher participation should be allocated to a project with a higher value, but this trend is reverted when the project value reaches a threshold. In addition, our developed optimal policy allows the platform to judiciously gain higher rewards even when the market is more volatile. Furthermore, extensive numerical studies have been conducted to glean rich managerial insights. Specifically, the optimal policy becomes more efficient even with more available designers, which is counterintuitive to the common sense that control should be more efficient while designers become scarcer; and the optimal policy is shown to be robust when the objective is changed from maximizing the total reward to maximizing the number of successful projects.

Tian, X., J. Shi and X. Qi, “Sequential stochastic allocation for creative crowdsourcing”, Production and Operations Management (POMS), forthcoming, 2021. 

*Business Week’s top 20 Premier Journal; Financial Times Research’s 45 Premier Journal, UT Dallas Listed top 24 journals.

 Business Analytics for Intermodal Capacity Management 

Network operations often suffer from chronic asset imbalance over time and across locations. This paper addresses the issue in the intermodal industry. The problem is mainly driven by myopic policies, environmental uncertainty, and network interdependence. To address the problem, we develop a unified framework that integrates two core operations: container repositioning and load acceptance. The central piece is the scarcity pricing scheme, which internalizes the externalities each acceptance imposes over time and across locations. The scheme plays two crucial roles: to transmit dynamic scarcity information and to incentivize container repositioning. It is most effective when network imbalance and supply risk are high. Exploiting random capacity and heterogeneous lead time, we further refine the load acceptance policy and develop efficient algorithms. We demonstrate that our approach can dynamically reduce network imbalance and improve efficiency. As such, our work provides analytical tools and insights on how to manage network capacity, when the information is dispersed and evolving over time.

Gao, L., J. Shi, M. Gorman and T. Luo, “Business Analytics for Intermodal Capacity Management”, Manufacturing & Service Operations Management (MSOM), 22(2), 310-329, 2020.

*Business Week’s top 20 Premier Journal; Financial Times Research’s 45 Premier Journal, UT Dallas Listed top 24 journals.

J. Shi, “Repositioning Empty Containers for Agricultural Container Logistics”, USDA sponsored Project, 2019, NJIT.