skip to main content


The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Friday, July 12 until 9:00 AM ET on Saturday, July 13 due to maintenance. We apologize for the inconvenience.

Search for: All records

Creators/Authors contains: "Arora, A."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. null (Ed.)
    his research examines supply chain collaboration effects on organizational performance in global value chain (GVC) infrastructure by focusing on GVC disaggregation, market turbulence, inequality, market globalization, product diversity, exploitation, and technological breakthroughs. The research strives to develop a better understanding of global value chains through relational view, behavioral, and contingency theories along with institutional and stakeholder theories of supply chains. Based on conflicting insights from these theories, this research investigates how relationships and operational outcomes of collaboration fare when market turbulence is present. Data is obtained and analyzed from focal firms that are engaged in doing business in emerging markets (e.g., India), and headquartered in the United States. We investigate relational outcomes (e.g., trust, credibility, mutual respect, and relationship commitment) among supply chain partners, and found that these relational outcomes result in better operational outcomes (e.g., profitability, market share increase, revenue generation, etc.). From managerial standpoint, supply chain managers should focus on relational outcomes that can strengthen operational outcomes in GVCs resulting in stronger organizational performance. The research offers valuable insights for theory and practice of global value chains by focusing on the GVC disaggregation through the measurement of market turbulence, playing a key role in the success of collaborative buyer–supplier relationships (with a focus on US companies doing business in India) leading to an overall improved firm performance. 
    more » « less
  2. null (Ed.)
    The study examines the relationship between the big five personality traits (extroversion, agreeableness, conscientiousness, neuroticism, and openness) and robot likeability and successful HRI implementation in varying human-robot interaction (HRI) situations. Further, this research investigates the influence of human-like attributes in robots (a.k.a. robotic anthropomorphism) on the likeability of robots. The research found that robotic anthropomorphism positively influences the relationship between human personality variables (e.g., extraversion and agreeableness) and robot likeability in human interaction with social robots. Further, anthropomorphism positively influences extraversion and robot likeability during industrial robotic interactions with humans. Extraversion, agreeableness, and neuroticism were found to play a significant role. This research bridges the gap by providing an in-depth understanding of the big five human personality traits, robotic anthropomorphism, and robot likeability in social-collaborative robotics. 
    more » « less
  3. null (Ed.)
    This paper aims to examines the moderating effect of small vs large supply base size on the relationship between strategic sustainable purchasing (SSP) and organizational sustainability performance (OSP). SSP is conceptualized as a dynamic capability consisting of strategic purchasing and environmental purchasing. Environmental collaboration is conceptualized as a mediator between SSP and OSP. Extant research has not examined the effect of the size of the supply base on the relationship between SSP and OSP. 
    more » « less
  4. Personalized learning environments requiring the elicitation of a student’s knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network models such as Deep Knowledge Tracing (DKT). Although DKT appears to be a powerful predictive model, little effort has been expended to dissect the source of its strength. We begin with the observation that DKT differs from BKT along three dimensions: (1) DKT is a neural network with many free parameters, whereas BKT is a probabilistic model with few free parameters; (2) a single instance of DKT is used to model all skills in a domain, whereas a separate instance of BKT is constructed for each skill; and (3) the input to DKT interlaces practice from multiple skills, whereas the input to BKT is separated by skill. We tease apart these three dimensions by constructing versions of DKT which are trained on single skills and which are trained on sequences separated by skill. Exploration of three data sets reveals that dimensions (1) and (3) are critical; dimension (2) is not. Our investigation gives us insight into the structural regularities in the data that DKT is able to exploit but that BKT cannot. 
    more » « less