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The institutional entrepreneurship implicit in a firm's sponsorship of its technology as a common standard is heset hy several challenges. These challenges arise from a standard's property to enahle and constrain even as potential competitors agree to cooperate on its creation. Our exploration of Sun Microsystems's sponsorship of its Java technology suggests that standards in the making generate seeds of self-destruction. Our study also identifies the social and political skills that a sponsor deploys to address these challenges.

Quality has come to be recognized as a strategic tool for attaining operational efficiency and improved business performance. This is true for both the goods and services sectors. However, the problem with management of service quality in service firms is that quality is not easily identifiable and measurable due to inherent characteristics of services which make them different from goods. Various definitions of the term ‘service quality’ have been proposed in the past and, based on different definitions, different scales for measuring service quality have been put forward. SERVQUAL and SERVPERF constitute two major service quality measurement scales. The consensus, however, continues to elude till date as to which one is superior. An ideal service quality scale is one that is not only psychometrically sound but is also diagnostically robust enough to provide insights to the managers for corrective actions in the event of quality shortfalls. Empirical studies evaluating validity, reliability, and methodological soundness of service quality scales clearly point to the superiority of the SERVPERF scale. The diagnostic ability of the scales, however, has not been explicitly explicated and empirically verified in the past. The present study aims at filling this void in service quality literature. It assesses the diagnostic power of the two service quality scales. Validity and methodological soundness of these scales have also been probed in the Indian context — an aspect which has so far remained neglected due to preoccupation of the past studies with service industries in the developed world. Using data collected through a survey of consumers of fast food restaurants in Delhi, the study finds the SERVPERF scale to be providing a more convergent and discriminant-valid explanation of service quality construct. However, the scale is found deficient in its diagnostic power. It is the SERVQUAL scale which outperforms the SERVPERF scale by virtue of possessing higher diagnostic power to pinpoint areas for managerial interventions in the event of service quality shortfalls. The major managerial implications of the study are: Because of its psychometric soundness and greater instrument parsimoniousness, one should employ the SERVPERF scale for assessing overall service quality of a firm. The SERVPERF scale should also be the preferred research instrument when one is interested in undertaking service quality comparisons across service industries. On the other hand, when the research objective is to identify areas relating to service quality shortfalls for possible intervention by the managers, the SERVQUAL scale needs to be preferred because of its superior diagnostic power. However, one serious problem with the SERVQUAL scale is that it entails gigantic data collection task. Employing a lengthy questionnaire, one is required to collect data about consumers' expectations as well as perceptions of a firm's performance on each of the 22 service quality scale attributes. Addition of importance weights can further add to the diagnostic power of the SERVQUAL scale, but the choice needs to be weighed against the additional task of data collection. Collecting data on importance scores relating to each of the 22 service attributes is indeed a major deterrent. However, alternative, less tedious approaches, discussed towards the end of the paper, can be employed by the researchers to obviate the data collection task.