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Journal Articles | 2024

Moderating effect of chief executive officer servant leadership on the relationship between entrepreneurial orientation and firm performance

Sanjay Chaudhary, Vishal K Gupta, Chitra Singla

Although entrepreneurial orientation (EO) is widely believed to benefit firms, it is increasingly considered necessary but insufficient for achieving superior firm performance. To better understand the circumstances under which EO is beneficial for firms, we adopt a resource-based perspective to introduce chief executive officer (CEO) servant leadership as a critical moderator of the EO–performance relationship. We validate our predictions using multi-point data from 170 small firms in India. The results reveal that three servant leadership behaviours – altruistic calling, wisdom and emotional healing – strengthen the EO–performance relationship. Furthermore, consistent with systems logic, the performance benefits of EO are found to be greater when the CEO servant leadership is closer to an ‘ideal’ configuration of behaviours. Overall, our findings contribute to a better understanding of the role of CEO leadership behaviours in fully actualising EO’s performance potential, thus illuminating the importance of aligning a firm’s strategic posture with other constructs of interest.

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Journal Articles | 2024

Advantages of foreignness and accelerator selection: A study of foreign-born entrepreneurs

Mohammad Fuad, Mohsen Mohaghegh, Shavin Malhotra

Foreign-born entrepreneurs are crucial for new ventures and regional growth. A key driver of their success is selection into business accelerator programs. We theorize that foreign-born founders with local residency and work experience are more likely to be selected by these programs. However, the institutional distance between an entrepreneur's host and the birth country reduces their likelihood of selection, whereas the entrepreneurial development of the host country increases it. We also examine the conditional effect of market learning capability. Evidence from 611 ventures in OECD countries supports our hypotheses, underlining the complex impact of foreignness on accelerator selection.

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Journal Articles | 2024

A machine learning approach to solve the E-commerce Box-Sizing Problem

Shanthan Kandula, Debjit Roy, Kerem Akartunali

E-commerce packages are notorious for their inefficient usage of space. More than one-quarter volume of a typical e-commerce package comprises air and filler material. The inefficient usage of space significantly reduces the transportation and distribution capacity increasing the operational costs. Therefore, designing an optimal set of packaging box sizes is crucial for improving efficiency. We present the first learning-based framework to determine the optimal packaging box sizes. In particular, we propose a three-stage optimization framework that combines unsupervised learning, reinforcement learning, and tree search to design box sizes. The package optimization problem is formulated into a sequential decision-making task called the box-sizing game. A neural network agent is then designed to play the game and learn heuristic rules to solve the problem. In addition, a tree-search operator is developed to improve the performance of the learned networks. When benchmarked with company-based optimization formulation and two alternate optimization models, we find that our ML-based approach can effectively solve large-scale problems within a stipulated time. We evaluated our model on real-world datasets supplied by a large e-commerce platform. The framework is currently adopted by a large e-commerce company across its 28 fulfillment centers, which is estimated to save the company about 7.1 million USD annually. In addition, it is estimated that paper consumption will be reduced by 2,080 metric tons and greenhouse gas emissions by 1,960 metric tons annually. The presented optimization framework serves as a decision support tool for designing packaging boxes at large e-commerce warehouses.

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Journal Articles | 2024

Research: Warehouse and logistics automation works better with human partners

René de koster, Debjit Roy

A study of automation usage in warehouse and logistics companies around the world suggests that blending human labor with robotics leads to greater efficiency than full automation alone. While scalable robotic systems can handle up to 1,000 tasks per hour, they often face limitations where additional robots don’t improve performance. Human-robot collaboration, employed by companies like DHL and CEVA, enhances productivity, reduces worker fatigue, and increases job satisfaction. The incremental approach of integrating human roles with automated systems not only keeps operations cost effective but also leverages human adaptability for continuous improvements.

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Journal Articles | 2024

Product market shock, stakeholder relationships, and trade credit

Jagriti Srivastava, Balagopal Gopalakrishnan, Rajesh Tharyan

The COVID-19 pandemic resulted in an extremely rare instance of a shock to global product markets. Using quarterly data for a sample of 7397 firms from 54 countries over the period 2017–2020, we study the causal impact of this shock on trade credit. Employing a difference-in-difference analysis, we find that, in contrast to findings in the literature on financial market shocks, low-credit quality firms are credit-rationed by their suppliers during a shock to product markets and that for low-credit quality firms, there is no substitution of trade credit with financial credit. Importantly, however, our analysis shows that low-credit quality firms with better stakeholder relations are able to obtain more trade credit than those with weaker stakeholder relations. Our results are robust to alternative definitions of key variables, alternative methodologies that address endogeneity concerns, a placebo test, stage of market development, and various levels of controls for unobserved heterogeneity. We show that trade credit is conditional on product market conditions and is not always a generous substitute for financial credit. However, maintaining good relations with stakeholders serves as an antidote to the adverse effect of product market shocks on trade credit.

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Journal Articles | 2024

Opening first-party App resources: Empirical evidence of freerRiding

Franck Soh, Pankaj Setia, Varun Grover

Platform owners are releasing their own apps on their platforms. These first-party apps (FPAs) typically leverage platform resources more effectively, competitively threatening rivals. Although the impact of FPAs on rivals’ innovation has been the subject of extensive study, the dominant view in previous research assumes that these FPAs are closed to third-party apps (TPAs). However, there is an increasing trend of FPAs opening their resources to TPAs, as they provide application programming interfaces (APIs) allowing TPAs to access their resources. Rivals still exist, as many TPAs choose not to have access to FPAs’ open resources because of their limited control over these resources. Does opening an FPA’s resources impact rivals’ innovation? The answer to the question is largely unknown. We exploit the release of the Apple Health Records API, a feature that opens Apple Health Records to TPAs, to design a quasi-experiment that investigates whether and how opening an FPA’s resources influence rivals’ innovations. Through several analyses, we conclude that opening an FPA’s resources to TPAs generates free-riding benefits for rivals. Moreover, these benefits mainly arise because of the growing presence of TPAs that do not adopt FPAs’ open resources in the market. We discuss the theoretical contributions and practical implications of our findings.

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Journal Articles | 2024

Multi-plant firms and the heavy tail of firm size distribution

Anindya S. Chakrabarti, Shekhar Tomar

The right tail of the firm size distribution has a heavy tail. The origin of this phenomenon, especially the specific characteristics of firms driving this pattern, remain a subject of extensive debate. Previous work has shown that plant size distribution has thinner tails than firm size distribution, indicating the role of multi-plant firms. However, we do not know whether this phenomenon is simply a mechanical effect arising from aggregation across multiple plants or whether the plants of multi-plant firms are different from those of single-plant firms. Using novel data with plant-to-firm mapping, we document that plants of multi-plant firms are more heavy-tailed than single-plant firms, indicating the dominance of the selection effect at the intensive margin. Extensive margin via aggregation of sales at the firm level plays a less crucial role than the selection effect. Importantly, single-plant exporters have a thinner tail than multi-plant non-exporters, suggesting a more dominant role of multi-plant identity than export identity in explaining heavy tails.

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Journal Articles | 2024

Addressing difficulties with abstract thinking for low-literate, low-income consumers through marketplace literacy: A bottom-up approach to consumer and marketing education

Madhu Viswanathan, Saravana Jaikumar, Arun Sreekumar, Shantanu Dutta, Adam Duhachek

We examine a bottom-up approach to consumer and marketing education for subsistence consumers, that is, those with low income and relatively lower literacy levels. They face a variety of cognitive and other constraints, with difficulty in abstract thinking being a central issue that is critical for effective decision-making. We study the impact of marketplace literacy education, with its unique bottom-up approach, on abstract thinking in the consumer domain. We test the effectiveness of a bottom-up educational approach, which covers concrete examples before abstract concepts, compared to the reverse sequence of a top-down approach. We find that the bottom-up approach in marketplace literacy education leads to more abstract thinking in the consumer domain compared to a top-down approach. We discuss the implications of this research for consumer affairs.

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Journal Articles | 2024

An exact method for trilevel hub location problem with interdiction

Prasanna Ramamoorthy, Sachin Jayaswal, Ankur Sinha, Navneet Vidyarthi

In this paper, we study the problem of designing a hub network that is robust against deliberate attacks (interdictions). The problem is modeled as a three-level, two-player Stackelberg game, in which the network designer (defender) acts first to locate hubs to route a set of flows through the network. The attacker (interdictor) acts next to interdict a subset of the located hubs in the designer’s network, followed again by the defender who routes the flows through the remaining hubs in the network. We model the defender’s problem as a trilevel optimization problem, wherein the attacker’s response is modeled as a bilevel hub interdiction problem. We study such a trilevel problem on three variants of hub location problems studied in the literature namely: p-hub median problem, p-hub center, and p-hub maximal covering problems. We present a cutting plane based exact method to solve the problem. The cutting plane method uses supervalid inequalities, which is obtained from the solution of the lower level interdiction problem. To solve the lower level hub interdiction problem efficiently, we propose a penalty-based reformulation of the problem. Using the reformulation, we present a branch-and-cut based exact approach to solve the problem efficiently. We conduct experiments to show the computational advantages of the above algorithm. To the best of our knowledge, the cutting plane approach proposed in this paper is among the first exact method to solve trilevel location–interdiction problems. Our computational results show interesting implications of incorporating interdiction risks in the hub location problem.

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Journal Articles | 2024

Women’s empowerment and intra-household diet diversity across the urban continuum: Evidence from India's DHS

Soumya Gupta, Payal Seth, Vidya Vemireddy, Prabhu Pingali

Women’s empowerment has been associated with improved nutritional outcomes in various settings. However, the gains from empowerment do not necessarily accrue to different members of the same household in the same manner. Furthermore, the relationship between empowerment and nutrition itself is likely to be shaped by the overall level of development in a given region. This paper investigates the heterogeneity in the association between women’s empowerment in nutrition index (WENI) and quality of intra-household diets between men and women when spatial variations in the levels of urbanization are accounted for, in India. We use intrahousehold dietary intake data for 60,000 men and women from the fourth round of India’s National Family Health Survey and conceptualize women’s empowerment using the women’s empowerment in nutrition index (WENI). We use geospatial data on nightlights as a proxy for the urban continuum. Nightlights intensity (NTL) captures the growth of smaller towns (between large urban cities and rural areas) that has characterized urbanization in India. A multilevel modeling approach indicates that a unit increase in WENI scores is associated with an improvement in women’s diet diversity scores by 0.19 units, with no significant association for men’s diet diversity. Heterogeneity analysis indicates that this finding holds at all NTL terciles. Alongside the role of WENI, we find that a doubling of NTL is associated with an increase in diet diversity scores by atleast 7–8% for both men and women, across wealth quintiles. These results emphasize the need for targeted approaches based on spatial heterogeneity in growth and development within a country when investing in the empowerment-nutrition pathway.

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