# How Does Manufacturing Strategy Contribute to Servitization Orientation? The Moderating Effect of Firm Size

Davide Gamba, Tommaso Minola, Matteo Kalchschmidt, Federico Adrodegari

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## To cite this version:

Davide Gamba, Tommaso Minola, Matteo Kalchschmidt, Federico Adrodegari. How Does Manufacturing Strategy Contribute to Servitization Orientation? The Moderating Effect of Firm Size. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.343-351, 10.1007/978-3-031-16407-1_41. hal-05386101

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# How Does Manufacturing Strategy Contribute to Servitization Orientation? The Moderating Effect of Firm Size

Davide Gamba<fnref n="1" />,<fnref n="2" />✉, Tommaso Minola<fnref n="1" />✉, Matteo Kalchschmidt<fnref n="1" />✉, and Federico Adrodegari<fnref n="3" />✉

**Abstract.** This study theoretically articulates and empirically validates a model of relationships between a firm's manufacturing strategy – proxied through competitive priorities – and servitization orientation. In addition, it analyzed the moderating effect of firm size on this relationship. The model was developed and tested through hierarchical regression analysis on the Sixth International Manufacturing Strategy Survey (IMSS-VI) data. The results indicate that different manufacturing strategies positively affect servitization orientation. In addition, firm size positively moderates the effect of exploitative manufacturing strategy. This research is one of the first quantitative studies that examine how different manufacturing strategies influence servitization orientation according to firm size.

**Keywords:** Manufacturing Strategy (MS) · Servitization orientation · Small-medium sized enterprises (SMEs) · Competitive priorities · International manufacturing strategy survey (IMSS)

## 1 Introduction

The fourth industrial revolution that dominates manufacturers’ innovation opportunities includes the concept of servitization [1], leading many product-centric firms to shift their business model from products to product-service systems [2]. Servitization assumes that companies that initially offer just products begin adding more and more services to their total offering [3].

Transitioning to a servitized business model requires an organizational-level orientation to services. The latter is depicted by the organization’s investments in new service development, employees’ upskilling for service delivery, and the development of products for enhancing service operations [4].

<footnote n="1">¹ Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio 7b, 24044 Bergamo, Dalmine, Italy
{davide.gamba,tommaso.minola,matteo.kalchschmidt}@unibg.it</footnote>

<footnote n="2">² MEI Srl, Via Ing. Caproni 50, 24036 Bergamo, Ponte San Pietro, Italy</footnote>

<footnote n="3">³ Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy
federico.adrodegari@unibs.it</footnote>
Companies must effectively define a clear manufacturing strategy to plan their long-range objectives [5]. Manufacturing strategy (MS) is a pattern of decisions to develop certain manufacturing properties as a competitive weapon to move from where it is to where it wants to be [6]. This definition implicitly emphasizes the competitive priorities concept, which refers to factors on which MS is based and how firms want to compete. Some competitive priorities reflect exploitative MS, which implies better products than competitors, while others reveal explorative MS that implies newer and more innovative products [7]. Firms that bring together exploitation and exploration are called ambidextrous [8].

Managers must allocate available resources to develop a subset of prioritized competitive priorities to win orders in the market [9]. In this sense, small and medium enterprises (SMEs) lack internal resources compared to large companies [10]. Thus, prioritizing competitive capabilities is crucial for SMEs to ensure servitization orientation. In addition, literature shows that servitization success is associated with appropriate slack resource allocations that guarantee to increase the chance of growing and obtaining positive service performance [11].

However, to the best of our knowledge, studies have been absent about the relationship between firms' competitive priorities (i.e., manufacturing strategy) and servitization orientation and the different outcomes for SMEs and large manufacturers. Thus, we focus on the relationship mentioned, emphasizing the moderating role of firm size. Based on quantitative data from the International Manufacturing Strategy Survey (IMSS), we developed the two hypotheses of this study (see Fig. 1):

<figure><img src="image_4.png" /><figcaption>Fig. 1. Hypothesized theoretical model.</figcaption></figure>

H1. The firm's exploitative/explorative MS's importance positively impacts the firm's servitization orientation.

H2. Firm size moderates the relationship between the firm's exploitative/explorative MS and the firm's service orientation. Specifically, as firm size increases (i.e.,
internal resources increase), the positive relationship between the firm's exploitative/explorative MS and the firm's service orientation is augmented.

The next section is devoted to the research model specification. Then empirical results are presented and discussed in Sect. 3. Last, final remarks and limitations are listed in the conclusion part.

# 2 Research Model Specifications

## 2.1 Sampling and Data Collection

The survey approach was chosen to test the proposed theoretical model as a suitable research method. Data from the sixth round of the International Manufacturing Strategy Survey (IMSS-VI) [12] were used to test the research hypotheses. IMSS is a periodic data gathering of a large and representative sample of worldwide manufacturing business units (ISIC 25–30). This initiative was launched in 1992 by the London Business School and Chalmers University. Data collection is carried out every 4 to 5 years through the cooperation of an international network of operations management scholars. IMSS-VI collected data between 2013 and 2014 in 22 countries, including 961 valid responses from 7,167 initial contacts. Previous studies have used the IMSS-VI data to analyze several servitization aspects [5, 8, 13–16].

We inspected all data items for any missing values. All variables considered include less than 5% of missing values. Thus, the missing values of a variable could be replaced with the respective mean of valid values if these were missing completely at random (MCAR) [17]. We performed Little's MCAR test [18] to verify this hypothesis. It is a $\chi^2$ test of the null hypothesis that the data are MCAR. The $\chi^2$ statistic was insignificant ($p = 0.32$), so the data could be considered MCAR.

## 2.2 Measures Development and Validation

Table 1 describes the model's three constructs based on IMSS-VI: exploitative competitive priorities, explorative competitive priorities, and servitization orientation.

<table><caption>Table 1. Constructs measurement.</caption><thead><tr><th>Construct</th><th>Item from IMSS-VI</th></tr></thead><tbody><tr><td>Exploitative MS<br><i>Cronbach's α proposed = 0.678</i></td><td>A3.b) Better product design quality (FL = 0.85)<br>A3.c) Better conformance to customer specifications (FL = 0.60)</td></tr></tbody></table>

(continued)
<table><caption>**Table 1.** (continued)</caption><thead><tr><th>Construct</th><th>Item from IMSS-VI</th></tr></thead><tbody><tr><td>Explorative MS<br><i>Cronbach's α proposed = 0.768</i></td><td>A3.j) Offer new products more frequently (<i>FL = 0.79</i>)<br>A3.k) Offer products that are more innovative (<i>FL = 0.79</i>)</td></tr><tr><td>Servitization orientation<br><i>Cronbach's α proposed = 0.816</i><br><i>Cronbach's α refined = 0.850</i></td><td>S3.a2) Expanding the service offering to your customers (e.g., by investing in new service development) (<i>FL = 0.89</i>)<br>S3.b2) Developing the skills needed to improve the service offering (<i>FL = 0.83</i>)<br>S3.c2) Designing products so that the after-sales service is easier to manage/offer (e.g., design for maintenance) (dropped after confirmatory factor analysis)</td></tr></tbody></table>

Quality was the competitive priority used to measure exploitative manufacturing strategy, while innovation was employed as a proxy to measure explorative manufacturing strategy [7–9]. Both were operationalized as reflective constructs and captured the importance of the competitive priority to win orders in the last three years using a 1 (none) to 5 (high) Likert scale. On the other hand, servitization orientation was operationalized as a reflective construct to describe the current implementation of service action programs using a Likert scale from 1 to 5 [5].

All constructs of the proposed model were subject to confirmatory factor analysis (CFA), providing overall acceptable model fit ($\chi^2/df = 40.391$, CFI = 0.986, TLI = 0.974, RMSEA = 0.053 [0.036, 0.071]), except for $\chi^2/df$ which shows that the model does not fit the data. Hence, we refined the model by dropping item S3.b2 from the servitization orientation construct reaching an acceptable model ($\chi^2/df = 8.861$, CFI = 0.998, TLI = 0.996, RMSEA = 0.022 [0.000, 0.051]). All items’ factor loading (FL) exceed the satisfactory level of 0.60. Cronbach’s alphas indicating that all constructs were reliable being near to or greater than 0.70.

Starting from Exploitative MS and Explorative MS constructs, we created a categorical variable to describe each business unit’s MS position according to the value of the two latent variables generated from CFA. Each respondent was clustered in one of the four groups in Table 2.

<table><caption>**Table 2.** MS categorical variable description.</caption><thead><tr><th>MS cluster</th><th>Description</th></tr></thead><tbody><tr><td>Steady<br><i>N = 319</i></td><td>Exploitative MS<sub>i</sub> &lt; Exploitative MS<sub>mean</sub> &amp; Explorative MS<sub>i</sub> &lt; Explorative MS<sub>mean</sub></td></tr><tr><td>Exploitative<br><i>N = 119</i></td><td>Exploitative MS<sub>i</sub> ≥ Exploitative MS<sub>mean</sub> &amp; Explorative MS<sub>i</sub> &lt; Explorative MS<sub>mean</sub></td></tr></tbody></table>

(continued)
<table><caption>**Table 2.** (continued)</caption><thead><tr><th>MS cluster</th><th>Description</th></tr></thead><tbody><tr><td>Explorative<br>N = 166</td><td>Exploitative MS<sub>i</sub> &lt; Exploitative MS<sub>mean</sub> &amp; Explorative MS<sub>i</sub> ≥ Exploitative MS<sub>mean</sub></td></tr><tr><td>Ambidextrous<br>N = 357</td><td>Exploitative MS<sub>i</sub> ≥ Exploitative MS<sub>mean</sub> &amp; Explorative MS<sub>i</sub> ≥ Exploitative MS<sub>mean</sub></td></tr></tbody></table>

A specific IMSS-VI item measures the business unit size, collecting last year's number of employees. According to the definition proposed by the European Commission [19], we create a dummy variable to discriminate between small-medium size (less than 250 employees) and large size (250 or more employees).

Last, following reviewed studies, we introduced three controlling variables as they could influence MS and servitization orientation. First, the business unit's industry was defined using five dummy variables (ISIC26, ISIC27, ISIC28, ISIC29, and ISIC30) [15]. Second, the business unit's country endowment was measured using the natural logarithm of the GDP per capita in 2012 obtained from the World Bank's databank [20]. Last, the region is operationalized as a dummy variable: developed countries (i.e., Europe and North America) versus developing countries (i.e., Asia and South America) [5].

## 3 Results

We employed hierarchical regression to analyze the data. It refers to the process of adding or removing predictor variables from the regression model in steps, which is an important method for revealing the moderating effect [21]. The model was developed in Stata 17.

The maximum variance inflation factor (VIF) among the observed variables was 1.80, indicating that multicollinearity was not problematic among the sample data. Table 3 shows the hierarchical regression results.

<table><caption>**Table 3.** Regression results of servitization orientation.</caption><thead><tr><th rowspan="3">Models</th><th colspan="2">Dependent variables</th></tr><tr><th colspan="2">Servitization orientation</th></tr><tr><th>Model 1</th><th>Model 2</th></tr></thead><tbody><tr><td><em>Independent variables</em></td><td></td><td></td></tr><tr><td>MS strategy - Exploitative</td><td>0.065**</td><td>0.008</td></tr><tr><td>MS strategy - Explorative</td><td>0.263***</td><td>0.274***</td></tr></tbody></table>

(continued)
<table><caption>**Table 3.** (continued)</caption><thead><tr><th rowspan="3">Models</th><th colspan="2">Dependent variables</th></tr><tr><th colspan="2">Servitization orientation</th></tr><tr><th>Model 1</th><th>Model 2</th></tr></thead><tbody><tr><td>MS strategy - Ambidextrous</td><td>0.333***</td><td>0.379***</td></tr><tr><td>MS strategy - Steady * Firm Size - Large</td><td>—</td><td>0.021</td></tr><tr><td>MS strategy - Exploitative * Firm Size - Large</td><td>—</td><td>0.090**</td></tr><tr><td>MS strategy - Explorative * Firm Size - Large</td><td>—</td><td>-0.045</td></tr><tr><td>MS strategy - Ambidextrous * Firm Size - Large</td><td>—</td><td>-0.045</td></tr><tr><td colspan="3"><strong>Control variables</strong></td></tr><tr><td>Industry ISIC 26</td><td>0.0.024</td><td>0.021</td></tr><tr><td>Industry ISIC 27</td><td>0.062*</td><td>0.056*</td></tr><tr><td>Industry ISIC 28</td><td>0.100***</td><td>0.096***</td></tr><tr><td>Industry ISIC 29</td><td>0.026</td><td>0.021</td></tr><tr><td>Industry ISIC 30</td><td>-0.027</td><td>0.025</td></tr><tr><td>Country - Per capita GDP</td><td>-0.137***</td><td>-0.135***</td></tr><tr><td>Region - Developing countries</td><td>0.157***</td><td>0.156***</td></tr><tr><td colspan="3"><strong>Regression indexes</strong></td></tr><tr><td>R<sup>2</sup></td><td>0.215</td><td>0.219</td></tr><tr><td>Adj R<sup>2</sup></td><td>0.201</td><td>0.208</td></tr><tr><td>F for the regression</td><td>26.01***</td><td>19.05***</td></tr></tbody></table>

Model 1 tested H1 analyzing the direct impact of MS strategy on servitization orientation. Results show that all MS strategy categories positively impact servitization orientation. In particular, business units that pursue ambidextrous MS are the more oriented toward servitization.

Model 2 tested H2 analyzing the moderating effect of firm size on the relationship between the firm's MS and service orientation. Results show that only the effect of exploitative MS is significantly moderated by firm size. The R² increased from model 1 to model 2 is poor, making clear that the proportion of variance explained by the introduction of firm size as moderator is very little. In order to reveal the nature of the moderating effect of firm size on the relationship between the firm's MS and service orientation, we plotted the predictive margins of the moderating effect of firm size in the exploitative MS case (see Fig. 2).

<footnote>* p < 0.10, ** p < 0.05, *** p < 0.01. Regression coefficients are standardized betas.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Predictive margins of exploitative manufacturing strategy on servitization orientation contingent on firm size.</figcaption></figure>

# 4 Conclusions

Servitization is challenging as firms need to reconfigure their business models, introduce new resources, and realign existing ones. This study investigated the role of MS – proxied through a firm’s competitive priorities – as a predictor of a servitization orientation.

Results show that exploration MS has a greater impact on servitization than exploitation MS. While exploitation leverages current resources to optimize products, exploration rely on new knowledge and partners to innovate manufactured goods. Firms more prone to explorative MS (e.g., launching a new product more frequently than their competitors) are more oriented to servitize their business model. Last, the analysis identifies that ambidextrous MS – the combination of exploitative and explorative MS – is the best predictor of firms’ servitization orientation.

Firm size explains the impact just of exploitative MS on servitization orientation. This result confirms that large firms have enough resources to invest in new service development and improve service-oriented skills than SMEs.

This paper is not exhaustive, and it presents a set of limitations. Due to the limited number of industries involved, the abovementioned findings cannot be fully generalized. In addition, IMSS-VI collected cross-sectional that did not catch the different firms’ behaviors over time. Last, the IMSS-VI dataset does not collect the recent fourth industrial revolution trends.

Based on the findings and limitations described, this study opens a set of potential further research. Firstly, given that manufacturing is rapidly growing, especially with digital technologies (e.g., cloud manufacturing), results should be confirmed by replicating analysis on more recent datasets that could include respondents from different industrial sectors (e.g., the construction industry). Secondly, the use of longitudinal datasets is
strongly suggested. Thirdly, future research opportunities arise by expanding the current proposed model. For example, the firm's servitization outcomes could be included by analyzing MS's impact on financial and non-financial service performance. Finally, the present work's findings should be deepened considering that SMEs are typically more servitization-oriented than large companies [22].

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