Service Strategy and Innovation Alignment: Study Critique

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Table of Contents

Article Summary
Critical Evaluation
Contribution to Knowledge and Implications
Suggestions to Improve the Research

Article Summary

The article analyzed in this paper is Exploring the alignment between service strategy and service innovation by Lightfoot and Gebauer. It investigates innovation service misalignment with related strategies, which limits new service releases and access to business opportunities in the capital goods sector. The authors’ central thesis is that reconfiguring service innovation determinants so that they are aligned with strategic goals could result in revenue gains.

The study used a qualitative, multi-case approach to explore service innovation-strategy fit of 12 firms (case studies) from Western Europe. Qualitative data were collected through interviews of 80 people and internal reports. Data analysis involved the pattern-matching logic and inductive reasoning to identify company-relate, process-related, and result-related determinants, e.g., innovation culture, service quality, and market competitiveness, and their strategy alignments. The findings suggest that achieving service strategies-determinants fit is not easy and innovation success is realized only with particular determinant schemes.

Critical Evaluation

Research Question

The article sought to answer the question; in what ways are service innovations aligned with strategic choices of capital goods producers? This research question seems right given that its purpose was to examine strategy-innovation alignment in service delivery. It fits with the other articles reviewed that show that specific configurations of service innovation factors translate into profitability. Previous studies examine this topic from different perspectives. While some scholars consider service innovations an efficient approach to providing customized solutions, others regard it as fundamental to the success of capital goods. Nevertheless, sparse research on how to adopt critical innovation factors hinders innovation-strategy alignment. The article sought to fill this knowledge gap by considering the different ways these determinants could be aligned with strategic choices to achieve business goals. However, it should have considered studies on retail service innovation to gain insights into their critical success factors and strategies.

Problem and Methods

The methods selected were largely appropriate for exploring the research problem. Investigating innovation determinants-strategy alignment would require a subjective dataset from people involved in innovation projects. The authors collected interview (qualitative) data from 80 interviewees drawn from R&D, marketing, and product/service departments. Interviewing allowed them to triangulate secondary data and validate constructs during the research process. The sampling strategy also matched the study problem; it ensured that only people working in innovation projects of 12 firms were recruited. However, the use of qualitative data from a few case studies makes the innovation-strategy alignments less generalizable across industries. A mixed-methods design would have allowed a more comprehensive inquiry and helped evaluate specific configurations quantitatively.

Reporting of the Findings

Overall, the study results are reported in a consistent and clear format. The specific determinants and innovation-strategy configurations identified from the qualitative data are presented in three separate sections: after-sale service, customer-support service, and development partners. Direct quotes from the interviews are included to support the findings. The authors include a table summarizing the key findings of their research. The article overlooked the demographic profile of the participants. The interviewees’ characteristics, including age, gender, position in the organization, and experience level are missing. Nevertheless, the article acknowledges and explains some of the study’s limitations. It identifies limited external validity attributed to qualitative data, emphasis on three service strategic options, and a small range of determinants as the main design weaknesses of the research.

Logic and Support for Claims

Overall, the article presents a logical argument about the alignments between innovation determinants and service strategic choices. It aggregates success variables into three paradigms (themes) and explains how they fit with specific strategies. It cites verbatim quotes from the respondents to support its claims. For example, when giving the reason for the inclusion of consumer value (success factor) in customer-support services, the authors include a convincing statement from a managerial interviewee on customer’s willingness to pay for optimized capital goods. However, quantitative data would have given strong and compelling support for the claims. One fallacy noted is the attribution of existing service strategies entirely to innovations. Other service-related functions, such as information technology, could also influence strategic choices of firms.

Personal Opinion

The article’s findings suggest an effective service strategy involves considerations for innovation and reconfigurations of its determinants. Their presentation is logical and clear. I agree with most of these findings. First, the finding that configurations of innovation variables vary with strategic choices is consistent with critical success factors that are context-specific. Therefore, the determinants for after-sales service must be different from those of customer support. Second, innovation success must only come from specific configurations. Ordinarily, firms must leverage on R&D to provide unique services or products to acquire competitive advantages in the industry. Thus, a right mix of service innovation determinants is critical to successful R&D efforts. However, I do not agree with the argument that similarity in the variables is due to the different theoretical frameworks in this area. I would attribute it to knowledge diffusion across different functions, organizations, and sectors due to the pressure to innovate and remain competitive.

Contribution to Knowledge and Implications

In my estimation, the article adds to the literature on service innovation by describing its determinants that are used in three strategies: after-sale, customer-support, and development partner services. It specifies the characteristics of each variable. Thus, firms can adopt these determinants in formal innovation processes to realize strategic gains. The article also contributes to theory development by establishing some differences in innovation success factors aligned with each service strategy. This finding challenges current theories on service innovation.

The study has significant practical implications for managerial decisions and strategic choices. First, in selecting a service strategy, firms must look at innovation and reconfigure related success factors for optimal results. Second, when changing a strategic alternative, organizations must reframe the determinants, as they differ between strategies. Thus, a firm moving to customer-support from after-sale service will need to include new factors in its service innovation project. The aim is to achieve an optimized innovation-strategy alignment that is critical to business success.

Suggestions to Improve the Research

The article relied on qualitative (interview) data to determine service innovation determinants for different strategies. Concerning research design, I would suggest a mixed-methods approach to obtain quantitative data that would help assess the innovation-strategy alignments identified from subjective themes and achieve external validity. A semi-structured questionnaire with validated measures and items can be used in data collection. The present study sampled 80 individuals working in innovation projects. While these participants have hands-on experience of service innovation, they may not know all determinants. I would suggest a larger, more diverse sample that includes employees, customers, and experts to obtain high-quality data. Further, a stratified random sampling of case studies drawn from multiple service sectors and locations can give results that can be generalized to many settings.

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