Welcome to our Winter 2023 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

LRI Faculty Seed Grant Proposal Reminder

Proposals for this year's Faculty Seed Grants are due March 1, 2023. Visit our application guideline page for more details.

Visit FSG Application Guidelines

1st Annual Real Estate Conference

NJIT's Paul Profeta Real Estate Technology, Design and Innovation Center will be holding its first annual Real Estate Symposium on March 2nd, 2023 from 8:30 AM to 3:30 PM in Eberhardt Hall. Join us to learn about recent innovations impacting the industry from internal and external New Jersey real estate leaders and experts.

Register Here

Research Spotlights

Jorge Fresneda Fernandez

How Inaccessible Retailers Websites Affect Blind and Low-Vision Consumers: Their Perceptions and Responses

This research seeks to fill a gap in the service and retailing marketplace experience literature as well as retailing practice by extending Attribution and Expectancy Disconfirmation Theories to the large and growing market of consumers with vision disabilities. It reveals how accessibility-related service failures with a retailer’s website can lead to anti-firm reactions from blind and low vision consumers, including social media sharing, negative word-of-mouth (NWOM) and avoidance of the retailer’s other sales channels even if they are accessible.

Blind respondents were recruited from national blindness organizations to participate in this study using a within-subjects design to test reactions to accessibility-related propositions in two different scenarios involving varying degrees of effort.

In both high- and low-effort conditions, an accessibility-related service failure leads to the anti-firm consequences of NWOM, social media sharing and avoidance of the retailer’s sales channels. Additionally, blind and low vision consumers who also feel inaccessible websites are discriminatory develop stronger anti-firm attitudes toward the offending retailers. Further, we aver that the retailer’s entire website including all its features, not just the homepage, should be made accessible to the growing market of vision-impaired consumers and thereby obtain substantial competitive advantages.

This research pertains to the service failure and recovery nomological network. It extends the existing paradigm to include accessibility-related service failures experienced by consumers with disabilities into the specialized category of discrimination-based service failures in instances where service recovery is not easily achieved. Empirical investigations of these experiences have been rare, despite the frequency with which they occur.

Market Segmentation in the Emoji Era

Organizations of every size are challenged with capitalizing on enormous amounts of unstructured organizational data—for instance, from social media posts—particularly for applications such as market segmentation. The purpose of this article is to give the reader an idea of the challenges and opportunities faced by businesses using market segmentation, including the impacts of big data. Our research will demonstrate what market segmentation might look like in the near future, as we also offer a promising approach to implementing market segmentation using unstructured data. With this demonstration, the article also illustrates how firms can develop specific actions or adjust their marketing mix based on unstructured data segmentation.

Fresneda, Jorge; Hui, Jeremy; and Hill, Chelsey, “Market Segmentation in the Emoji Era.” Communications of the ACM. 65, 4, 105–112, 2022. https://doi.org/10.1145/3478282

Hindy Schachter

Race, Class, Gender and Social Entrepreneurship: Extending the Positionality of Icons

In the mid twentieth century feminist scholars of management and social science showed that fields such as social entrepreneurship and public administration that seemed to have had almost exclusively male leadership in the Progressive Era also had gained from the pioneering insight of women aligned with settlement houses and scientific management. This research had a radical impact on extending the genders represented among icons in extensive fields. More recently, however, emphasis on intersectionality has shown that women have multiple intersecting identities; middle class women cannot necessarily represent the interests of working class women and white women cannot represent the interests of Black or Native American women. As the twentieth century research focused on middle class or elite women, additional work was needed to extend the positionality of icons by race and class. This research begins this extension with analyses of the contributions of Josephine St. Pierre Ruffin, a leader in Massachusetts’ Progressive era Black community and Rose Schneiderman, a labor union activist. The research explores how these leaders interacted with elite white women reformers and with male allies.

Schachter, Hindy L., “Race, Class, Gender and Social Entrepreneurship: Extending the Positionality of Icons,” Journal of Management History 2022, 28, 4, 476-490. https://doi.org/10.1108/JMH-11-2021-0059

Chase Wu

Intelligent Computational Steering and Model Validation Through Machine Learning and Visualization

Simulation data are rich in potential for accelerating scientific discovery, but are yet untapped owing to a lack of an effective infrastructure for computational steering and associated analytical tools. We are tackling this national-scale open problem by developing solutions that will advance the state of the art with respect to existing scientific processes and related cyberinfrastructure solutions. Specifically, we will develop a web-based platform for: i) intelligent parameter selection and tuning strategies, by leveraging information theory, machine learning, and stochastic approximation, and ii) scalable visual analytic techniques that enable scientists to rapidly accelerate their conventional model calibration and selection processes involving many simulations. We envision that the resulting technology will revolutionize existing ad-hoc or stand-alone steering methods, and transform the disconnected nature of parameter tuning and model calibration processes through disruptive yet proven data-driven solutions.

Ensemble Learning Models for Large-Scale Time Series Forecasting in Supply Chain

Traditional statistical approaches that dominate time series forecasting suffer from model inaccuracy and performance limitation. We propose a class of ensemble techniques by stacking neural networks and baseline models to address such challenges. First, we conduct classification and segmentation based on feature engineering of signal components, such as spikes and anomalies as outlier skews, to manifest the combined scenario complexity on categorical data hierarchy and identify the patterns for ensemble forecasting. Then, we employ an ensemble model with time series pattern sensors to automatically differentiate signal components, including seasonality, promotion, trends, and intermittent and discontinued activities. The proposed ensemble modeling approaches demonstrate a considerable performance improvement in terms of accuracy over individual baseline models and other univariate time series algorithms.

W. Liu, Q. Ye, C.Q. Wu*, Y. Liu, X. Zhou, and Y. Shan, "Machine Learning-assisted Computational Steering of Large-scale Scientific Simulations." In Proceedings of the 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, New York, USA, October 1-3, 2021 (ISPA21). https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00138

Q. Ye, W. Liu, and C.Q. Wu*, "NoStop: A Novel Configuration Optimization Scheme for Spark Streaming." In Proceedings of the 50th International Conference on Parallel Processing, Argonne National Laboratory in Chicago, IL, USA, August 9-12, 2021 (ICPP21, acceptance rate: 26.4%). https://doi.org/10.1145/3472456.3472515

H. Alquwaiee and C.Q. Wu*, "On Performance Modeling and Prediction for Spark-HBase Applications in Big Data Systems." In Proceedings of IEEE International Conference on Communications, Seoul, South Korea, May 16-20, 2022 (ICC22). https://doi.org/10.1109/ICC45855.2022.9838762