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It is generally accepted that leadership is the foundation for creation of a company’s business results. However, empirical evidence is lacking. This paper examines the relationship between leadership and key performance results based on Return on Leadership Model. The model links Denison’s four leadership traits to people results, customer results, and ultimately key performance results. Further specified, the model is a structural equation model with nine latent variables, each measured by a set of indicators, observed by survey questions. To validate the model, an empirical study was conducted, and 379 managers from medium‐sized and large companies in Denmark participated. The estimation of the model shows that the model structure explains return on leadership very well, and the findings support the developed model. We are able to quantify the relationships from leadership to people results, customer results and key performance results and, in this way, the data presented here provide evidence that leadership is linked to key performance results. Several results and applications of the model are shown. The findings provide new knowledge about how leadership can be linked to performance, and how excellent leadership creates key performance results.

The purpose of this paper is to investigate whether the different approaches to the classification of quality attributes deliver consistent results.

The investigation includes four approaches and enables comparisons to be made from a methodological perspective and from an output perspective. The different approaches are described, analyzed, and discussed in the context of an empirical study that investigates how 430 respondents perceive the performance of an e‐service. The theory of attractive quality rests on a solid theoretical foundation and a methodological approach to classify quality attributes. Recently, various authors have suggested alternative approaches to the traditional five‐level Kano questionnaire – including a three‐level Kano questionnaire, direct classification, and a dual‐importance grid.

The classification of quality attributes are found to be dependent on the approach that is utilized. The development of new ways to classify quality attributes should follow rigid procedures to provide reliable and consistent results.

This is the first attempt to compare alternative approaches to classify quality attributes. For managers, our results provide guidance on what approach to choose based on the strengths and weaknesses with the different approaches.

This study aims to examine the influence of relational capital quality on client loyalty, comprising both behavioral and attitudinal, in the pharmaceutical industry of Pakistan.

The partial least squares technique is used to test the relationship using a sample of 111 pharmaceutical firms. We applied a non-parametric procedure, the bootstrapping method, to estimate the coefficient path of the relationships. Appropriate construct measures were used based on past studies to measure the dimensions of relational capital quality and client loyalty.

The findings suggest that relational capital quality significantly affects client loyalty. All three dimensions of relational capital quality, commitment, satisfaction and trust, have a significant and positive influence on both attitudinal and behavioral loyalty. However, client satisfaction is found to exert the strongest impact on behavioral and attitudinal loyalty.

It is important for the pharmaceutical firms in Pakistan to improve client satisfaction to establish behavioral loyalty and sustain their clientele base. Trust and commitment should be managed independently, depending on the focus of firms, either attitudinal loyalty or behavioral loyalty.

This study is among the few that was able to empirically examine the role of various dimensions of relational capital quality in influencing clients’ attitudinal and behavioral loyalty. In addition, the study uses a new firm-level data set, compiled from a survey of the pharmaceutical industry in Pakistan, which is currently facing challenges in terms of customer–supplier sensitivity.