Measuring customer involvement in new service developments

Service marketing managers are being required to develop new services that succeed in the market and are valuable for customers. Services Marketing literature therefore stresses the need to innovate with customers and to integrate their view into the new service developed. However, consensus about the positive effects of customer involvement in new service development (NSD) has not been reached. Drawing on the theory of organisational knowledge creation and the concept of marketing orientation, we argue that customer involvement is not related to successful new services per se. However, we propose it contributes to the increase of a firm’s customer knowledge stock, the tacit and explicit dimension. The study results demonstrate that the increase in a firm’s tacit customer knowledge stock significantly affects market success, project success and sustainable competitive advantage, while the increase of explicit customer knowledge stock positively influences the acceptance of new service concept ideas initiated by customers. Both the explicit and tacit customer knowledge stock is positively influenced by the level of customer involvement. Furthermore, the internal resource-based antecedents to customer involvement decisions are investigated. Our findings illustrate that a firm’s prior tacit knowledge about customers inhibits integration of customers in NSD, whereas prior explicit customer knowledge positively affects customer involvement. As for market-driven NSD, customer orientation, and project innovativeness, the study shows different effects on stages of customer involvement. Finally, the research reveals that service firms manage customer involvement differently related to the facets of the construct, namely (1) methods and (2) stages of customer involvement. Distinct management approaches are compared and contrasted to unearth salient decision parameters. The research is based on interviews, one expert survey and one main survey, incorporating 131 respondents of diverse service firms in nine countries. Responses have been analysed in two structural equation models by Partial Least Squares (PLS) and explored by cluster analysis. We suggest that Service Marketing managers should be more attentive to the act of customer knowledge creation to manage customer integration in NSD effectively. Particularly, they should be aware of the role of tacit customer knowledge in order to develop successful new services. A tight customer orientation is not worthwhile throughout NSD, since new markets may be neglected when working too close with current customers. Furthermore, contrary to prevalent research, NSD executives should combine beneficial methods of customer involvement instead of focusing on one method. Using different methods helps managers to create divergent perspectives on customer preferences and needs, required to generate new ideas. Finally, we propose that customer involvement in NSD should not be seen as a short-term investment

Ansätze einer Algorithmischen Anwendung Quantititiver Verfahren zur Effizienten Bedarfsprognose von Vorprodukten. Erste Ergebnisse Einer Empirischen Untersuchung

Zufällig schwankende Nachfragen nach Vorprodukten bzw. Teilen und Komponenten machen die Verwendung von stochastischen Modellen der Lagerhaltung notwendig. Das vorliegende Papier beschreibt einen standardisierten algorithmischen Ansatz, mit dem der Verbrauch von Vorprodukten für die Zeiträume von drei, sechs oder zwölf Monaten mit Hilfe zeitreihenökonometrischer Verfahren prognostiziert werden kann. Im Rahmen dieses Ansatzes werden für jede Vorproduktgruppe die unterschiedlichsten quantitativen Prognosetechniken angewendet. Zu den Techniken zählen unter anderem AR-, MA-, ARMA-, ARIMA- und strukturelle Regressionsmodelle. Durch algorithmisches Vorgehen wird aufgrund von Gütekriterien (z. B. die Prognosefähigkeit in einem Testdatensatz) ein optimales Prognosemodell ermittelt, das für die Prognose des Bedarfs verwendet wird. Für alle gewählten Prognosezeiträume erwies sich das ARMA-Modell der d-differenzierten Zeitreihe als bestes Prognosemodell, gefolgt von einfachen Moving Average und ARIMA-Modellen. Die Bedeutung autoregressiver Verfahren nimmt aber mit der Länge des Prognosezeitraumes ab. Strukturelle Ansätze erweisen sich allerdings fast nie als beste Prognosemodelle, auch wenn deren Bedeutung mit der Länge des Prognosezeitraumes zunimmt. Der algorithmische Ansatz ermöglicht für einen erheblichen Teil (rund 60 Prozent) der Vorprodukte eine gute Prognosequalität. Die Güte der Prognose verbesserte sich, je seltener Zeiträume mit fehlender Nachfrage auftreten. Bei Beachtung ausgearbeiteter Voraussetzungen, dürfte diese algorithmische – und daher einfach durch den Computer zu ermittelnde – Vorgehensweise, die praktische Aufgabe der Prognose von Lagerabflüssen für einen erheblichen Teil von Vorprodukten bzw. Teilen und Komponenten vereinfachen.Inventory Management, Forecasting, Material Requirement Planning, Time Series

Potentials of on-line repositioning based on implanted fiducial markers and electronic portal imaging in prostate cancer radiotherapy

<p>Abstract</p> <p>Background</p> <p>To evaluate the benefit of an on-line correction protocol based on implanted markers and weekly portal imaging in external beam radiotherapy of prostate cancer. To compare the use of bony anatomy versus implanted markers for calculation of setup-error plus/minus prostate movement. To estimate the error reduction (and the corresponding margin reduction) by reducing the total error to 3 mm once a week, three times per week or every treatment day.</p> <p>Methods</p> <p>23 patients had three to five, 2.5 mm Ø spherical gold markers transrectally inserted into the prostate before radiotherapy. Verification and correction of treatment position by analysis of orthogonal portal images was performed on a weekly basis. We registered with respect to the bony contours (setup error) and to the marker position (prostate motion) and determined the total error. The systematic and random errors are specified. Positioning correction was applied with a threshold of 5 mm displacement.</p> <p>Results</p> <p>The systematic error (1 standard deviation [SD]) in left-right (LR), superior-inferior (SI) and anterior-posterior (AP) direction contributes for the setup 1.6 mm, 2.1 mm and 2.4 mm and for prostate motion 1.1 mm, 1.9 mm and 2.3 mm. The random error (1 SD) in LR, SI and AP direction amounts for the setup 2.3 mm, 2.7 mm and 2.7 mm and for motion 1.4 mm, 2.3 mm and 2.7 mm. The resulting total error suggests margins of 7.0 mm (LR), 9.5 mm (SI) and 9.5 mm (AP) between clinical target volume (CTV) and planning target volume (PTV). After correction once a week the margins were lowered to 6.7, 8.2 and 8.7 mm and furthermore down to 4.9, 5.1 and 4.8 mm after correcting every treatment day.</p> <p>Conclusion</p> <p>Prostate movement relative to adjacent bony anatomy is significant and contributes substantially to the target position variability. Performing on-line setup correction using implanted radioopaque markers and megavoltage radiography results in reduced treatment margins depending on the online imaging protocol (once a week or more frequently).</p