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Do female CEOs face a compensation gap? The purpose of this paper is to examine whether gender affects the total compensation of today’s CEOs, and whether it moderates ten factors influencing their total compensation.

Taking the 54 female CEOs cited in the US 2014 Fortune’s 1000 report, a matched sample of male CEOs was selected, matched according to the crosstab of age by education and by the sizes of the companies directed by these female CEOs.

Using four years’ worth of Fortune reports, between 2013 and 2016, this matched sample indicates that female CEOs are not discriminated against in terms of total compensation. However, eight factors do show a significant effect on total compensation. Using moderation analysis, the present study reveals how gender interacts with company size, sector, membership of outside boards and nature of previous experience.

This paper addresses an important and under-researched gap, with contradictory findings in the existing literature, by compiling and testing the characteristics of male and female CEOs which are not cited in Fortune 1000 reports.

Arguably, this is therefore one of the first papers to study gender differences in total compensation among Fortune 1000 CEOs using a matched sample technique, based on a larger number of female CEOs and a larger number of years than any previous research.

A multinational corporation (MNC) looking to locate within a cluster is mainly interested in gaining access to scarce and highly valuable tacit knowledge. The transfer of such resources first requires sharing a certain degree of architectural and component knowledge. The social, organizational, cultural, institutional, technological, and physical distances between a MNC and the new subsidiary, as well as its local partners, offer a good indication of the way the architectural and technological knowledge is shared. The purpose of this paper is to examine the transfer of systemic technological expertise (component tacit knowledge) that is incorporated into organizational practices (architectural knowledge).

The mathematical expression of localization performance is inferred from a conceptual research that formulates hypotheses regarding the impact of these variables on knowledge transfer. The MNC chooses its location in such a way as to maximize this performance.

This research contributes to a better understanding of how knowledge transfer effects may interact with local effects, while explaining a subsidiary’s performance with regard to location.

In order to apply this model, one would need to numerically compute the variables of this model and the performance in order to obtain a numerical estimation of the variables, by the econometric methods, which intervenes in the performance. Then one could use this numerical expression of the performance as a specific criterion of localization. Indeed it would be sufficient to evaluate both architectural and component knowledge which could be exchanged as well as different distances and the motivation for each of possible localizations and to select the one which gives the maximal numerical value for the performance.

The authors deduced from the mathematical model a simple decisional criterion for a manager in search of an optimal location.

This research provides an interpretation of the concept of knowledge embeddedness by showing that the effective transfer of architectural and component knowledge involves the prior sharing of a certain amount of this knowledge.

A multinational company (MNC) looking to locate within a cluster is mainly interested in gaining access to scarce and highly valuable tacit knowledge. The transfer of such resources first requires sharing a certain degree of architectural and specific knowledge. This paper aims to examine the transfer of systemic technological expertise (specific tacit knowledge) that is incorporated into organisational practices (architectural knowledge). To quantify the level of knowledge transfer involved, the present study defines the architectural distance between the MNC and the cluster.

The mathematical expression of acquisition performance is inferred from a conceptual study that formulates hypotheses regarding the impact of these variables on knowledge transfer. The MNC chooses its location in such a way as to maximise this performance.

Applying a mathematical model to knowledge transfer between two of the MNC units helps to determine if the locally acquired knowledge could benefit other units of the MNC.

The present study defines the architectural distance between the MNC and the cluster. This architectural distance is defined by a vector composed of social, organisational, cultural, institutional, technological and geographic distances between the new acquisition and its network of local partners, on the one hand, and the MNC, on the other. Knowledge transfer also depends on the business players’ trust and motivation. Further research through a quantitative study would be useful to improve the links between the proposed mathematical model and the efficiency of an MNC’s location within a cluster.

The solution to the optimisation problem allows to put forward a simple decision criterion to assist a manager who has to face the problem of an optimal location choice.

First, this study contributes to a better understanding of how knowledge transfer effects may interact with cluster effects, while explaining a subsidiary’s performance with regard to location. Second, it provides an interpretation of the concept of knowledge embeddedness by showing that the effective transfer of architectural and specific knowledge involves the prior sharing of a certain amount of this knowledge.

The purpose of this paper is to draw attention to the link between knowledge transfer flow and the location of a multinational corporation (MNC).

The authors put forward a conceptual approach to formulate the mathematical modelling of a firm’s performance following the decision to join a regional cluster. This model builds on a recent stream of theoretical literature which has investigated the relationship between networks and the creation and diffusion of knowledge. The purpose of this model is to propose a mathematical tool to determine the long-term financial results induced by knowledge transfer from an MNC’s acquired subsidiary located in a cluster to another part of the MNC.

This study has several important research implications. First, it is a useful step towards a better understanding of how knowledge transfer effects may interact with cluster effects, while explaining subsidiary location performance. Second, it focuses on the most valuable, often highly tacit knowledge competencies.

Other investigations would certainly be welcome to improve the links between the proposed mathematical model and the efficiency of the location of an MNC in a cluster through a quantitative study.

The authors constructed this study with the aim of developing a model that would give us a better understanding of the impact of embedded knowledge on the efficiency of a localization choice made by an MNC.

To date, there has been little in the literature on the profit arising from a multinational firm’s choice of location.