Digital platforms, focused on service delivery, are swiftly gaining popularity in urban India. However, automated mechanisms like algorithmic pricing deployed by such platforms raise concerns about algorithmic opacity, fairness, data privacy, regulatory difficulties, among others. This paper presents the first exploratory study examining pricing differential, if any, observed by different individual users of two popular ride-hailing platforms (namely, Uber and Ola) in India. Based on relevant global research and consumer experiences, it identifies certain factors and proposes a simply model to study algorithmic pricing in digital ride-hailing. This study uses data collected from a survey of 138 respondents, and finds substantial variation in prices quoted by ride-hailing platforms for the same ride. Besides expected factors, certain individual-specific characteristics (including, group identifiers) also statistically affect pricing. Overall analysis reveals that price determination on ride-hailing platforms is not a neutral demand-supply matching exercise. The study suggests policy measures and highlights the need for regulation regarding data privacy, and algorithmic audits, for ensuring fairness and transparency in the sector.

Although prior research conceptualizes the multinational enterprise (MNE) as a network of organizational units located in different countries, extant knowledge of how the MNE’s network evolves is limited. Consistent with studies highlighting the important role of political relations for an MNE’s decision to enter or exit a host country, we examine whether the political relations between countries in the MNE’s network (rather than solely between the MNE’s home and host country) influence the evolution of the MNE’s network. We test our theory by analyzing data on the host countries of 1,126 Japanese MNEs with a stochastic actor-oriented model (SAOM). We find that the political relations of host countries in the MNE’s network determine whether a focal host country becomes or stays part of the MNE’s network. This study advances prior research which largely examines the role of the political relations between the MNE’s home and a host country for an MNE’s decision to enter or exit a host country, by showing that, in fact, it is the political relations between the host country and other host countries in the MNE’s network which determine the MNE’s network evolution. Without accounting for this effect, conclusions on how political relations affect an MNE’s network evolution may be flawed and misleading. Methodologically, using a SAOM helps to overcome model misspecifications in current research because it allows for modelling interdependencies in an MNE’s network evolution. These interdependencies are not accounted for in commonly applied regression models, leading to potentially biased estimates.