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In the context of a retailer with an international supply network, this paper develops theories of (a) how both stability and strong ties in an international supply network combine to yield a resource base that enables the development of flexible relationships with suppliers, (b) how stability and relationship flexibility in the international supply network of a retail reseller may in turn be driven both by the international diversity and by the density of the retailer’s supply network in a product category, and (c) how both the international diversity and the density of a retailer’s supply network may directly affect the likelihood of a retailer developing flexible relationships with its supplier. In doing so, this paper develops and presents six hypotheses and discusses some approaches to measurement of the underlying constructs and testing the hypothesized effects.

This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

In this reply, we respond to a series of substantive comments on our work by both Madhavan and Walker. In our response to Madhavan’s comments, we consider three accounts – “weak,” “semi-strong” and “strong” – that clarify how our model “explains” and offers insights that can emerge from our modeling strategy. We also explore ways in which our explanatory strategy might be extended in light of his critique. In our response to Walker’s comments, we adopt the “semi-strong” thesis, which admits variation in network-generating mechanisms, while also recognizing that information needs to be distributed and shared in order for many types of networks to function.

This commentary addresses the problem of interfirm network formation from the perspective of multiple types of relationship content and of network structure. The approach builds on Burt’s (1980) typology of network structures and on a range of empirical studies on interorganizational networks. The chapter by Moldoveneau, Baum and Rowley on network evolution from an information-sharing perspective captures part of this research domain. The challenges posed by network evolution research are discussed in the broader light of multilevel analysis.

This chapter explains how to design and operationalize a knowledge network analysis. The authors walk through a nine-step methodology that addresses each stage of the process. The nine-step process is the result of an in-depth review of the theoretical and applied literature. The authors explain how and why each step contributes to the quality and goodness of the analysis. The risks of skipping or sub-optimizing steps are explained. The step-by-step process highlights the dependence of a knowledge network analysis on data sources. The authors explain the importance of identifying, collecting, and curating sources.

The Moldoveanu, Baum & Rowley approach highlights the importance of developing a multi-level theory of network evolution that explicitly accounts for strategic agency and (bounded) rationality. I discuss intriguing questions raised by their model about structure vs. topology, feedback loops in evolution, and network resources other than information.