Multiple recipes for success – A configurational examination of business portfolio restructurings

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Abstract

This explorative empirical study investigates which sets of portfolio restructuring design characteristics, contingent on individual firm and industry characteristics, lead to successful business portfolio transformations. We draw on insights from the strategic change, strategic fit, and organizational turnaround literature and develop a contingency model for transformation success. To allow for complex configurations, we employ a set-theoretic approach based on fuzzy set qualitative comparative analysis (fsQCA). Our findings suggest that there is no single best strategy for restructuring a portfolio. Rather, there are multiple pathways to success in portfolio transformation, depending on a firm's competitive posture and industry environment. In particular, our results highlight the pivotal influence of prior firm performance and environmental jolts on the design of successful business portfolio restructuring (BPR) strategies.

Introduction

Actively managing the business portfolio to align firms’ businesses with their environment is a key element of corporate strategy (Pathak et al., 2014, Wu and Delios, 2009). In this context, large-scale transformations of the business portfolio mark the most decisive form of strategic change and constitute turning points in the life of a company (Nippa, Pidun, & Rubner, 2011). These transformations of the business portfolio, so called business portfolio restructurings (BPR), are defined as the implementation of a new portfolio strategy that transforms the composition of the portfolio of business units held by a multi-business firm. These portfolio restructurings are the major form of corporate restructuring (Schönhaar, Nippa, & Pidun, 2014) (together with financial and organizational restructuring) and can involve change in the form of e.g., acquisitions, divestitures, spin-offs, or liquidations (C. Park & Kim, 2008). Previous literature about corporate restructuring could for instance show that firm characteristics such as business relatedness (Bergh, Johnson, & Dewitt, 2008), corporate governance characteristics such as the ownership and board structure (Dittmann et al., 2008, Jung et al., 2015, Kavadis and Castañer, 2015, Perry and Shivdasani, 2005), and industry characteristics such as shocks (Powell & Yawson, 2005) are relevant antecedents of corporate restructuring. In total, corporate restructuring makes about nine percent of strategy literature (Furrer, Thomas, & Goussevskaia, 2008). However, in the extant literature there is no consensus on the main determinants of the success or failure of such business portfolio restructurings (understood as the performance effect of the restructuring).

This observation is supported by studies reviewing the specific BPR phenomena divestitures (Brauer, 2006, Lee and Madhavan, 2010) and corporate refocusing (Hoskisson et al., 2005, Johnson, 1996, Rondi and Vannoni, 2005), which depict the difficulties to gain a comprehensive overview of the main success determinants. For example, some researchers find refocusing strategies to generally enhance firm performance (Hoskisson and Johnson, 1992, Markides, 1995), while others provide evidence of negative post-restructuring performance of refocusing firms (Montgomery & Thomas, 1988) or find no evidence for a general diversification discount or refocusing premium (Colak, 2010). Other researches emphasize the need for contingency models that account for the strategic fit of firms’ environment and the direction, magnitude, and timing of strategic change (Zajac, Kraatz, & Bresser, 2000).

These mixed findings in prior research may stem from the complex interplay of internal and external factors that influence strategic divestitures (Brauer, 2006), strategic change (Oehmichen, Schrapp, & Wolff, 2017), and portfolio transformation processes in general (Liao, 2005), as well as their respective success or failure. In different industry environments reasons for transformations might differ and hence different configurations of restructuring determinants can affect transformation success (Bergh, 1998). It thus seems that “the area of corporate restructuring is complex as it occurs across all industries over considerable periods of time and has multiple implications. Research questions using simple univariate models may not detect these complex relationships” (Johnson, 1996, p. 478).

Scholars examining the related fields of strategic change (Klarner and Raisch, 2013, Rajagopalan and Spreitzer, 1997), strategic fit (Zajac et al., 2000), or organizational turnaround (Trahms, Ndofor, & Sirmon, 2013) have developed a profound understanding of how the interplay of environmental factors such as the environmental dynamism (see e.g., Heyden et al., 2013, Van Doorn et al., 2017) (e.g., industry change), firm characteristics (e.g., profitability), and strategic actions (e.g., divestitures) determine the success of change processes. However, equivalent insights regarding more complex restructurings of entire business portfolios remain scarce. We aim to take the first step toward filling this gap by employing a theory-building research approach to better understand the configurations of determinants of portfolio transformation success. Specifically, we seek to answer the following question: Which sets of transformation design characteristics, contingent on individual firm characteristics and industry environment, will lead to successful BPR?

We develop a contingency model that describes how the combinations of firm characteristics, industry environment, and transformation design influence BPR success. To explore combinations of firm, industry, and transformation factors with high transformation success we use fuzzy set qualitative comparative analysis (fsQCA), which is intended not to isolate independent effects of single explanatory factors on a particular outcome, but rather to identify the multiple configurations of factors that lead to a particular outcome (Ragin, 2008).

Our study contributes to corporate restructuring research and the related literature in several ways. First, our findings demonstrate that there is no single universal bundle of transformation design characteristics that leads to successful portfolio transformations. We present multiple paths to BPR success, contingent on a firm characteristics and industry environment. Thereby, our study builds on first contingency models of strategic fit and extends them by allowing the multidimensional interplay of contingency factors on firm and industry level and transformation characteristics. While previous studies could already show with two-dimensional regression models that environmental factors matter for the performance effect of transformation characteristics such as direction, magnitude, and timing (e.g., Zajac et al., 2000), our study allows the design of specific configurations covering multiple contingency factors and transformation characteristics. Hence, we are able to make recommendations such as that firms with weak prior performance and low prior diversification with an environmental jolt should choose portfolio diversification that is fast and of moderate magnitude. Thus, our results also offer vital practical recommendations for managers that are planning and conducting a portfolio transformation. Second, our study aids to untangle mixed prior findings on the determinants of transformation success, and in this regard, we take the first step toward building a more holistic model of the complex interplay of internal and external factors in BPR. Third, our results highlight the causal asymmetry in BPR success in that we show how the factors leading to successful portfolio transformations differ from those leading to unsuccessful BPR.

The remainder of the paper is organized as follows. First, we provide a brief recap of the concept of BPR and craft a contingency framework for BPR success, building on existing models from the strategic change, strategic fit, and organizational turnaround literature. We then review the corporate restructuring literature to identify and depict the most relevant determinants of transformation success regarding firm characteristics, industry environment, and transformation design characteristics. Finally, we introduce our analytical procedure and discuss results, conclusions, limitations, and recommendations for future research.

Section snippets

Theory

As mentioned earlier, we define BPR as large-scale and strategic modifications of the portfolio composition of strategic business units held by a multi-business firm. These kinds of corporate actions have been of great relevance for both scholars and practitioners since the middle of the past century (Nippa et al., 2011, Schönhaar et al., 2014). Current studies show that corporate portfolio management remains an important item on the strategic agenda of most large multi-business firms (Bergh

Method

The simultaneous consideration of transformation design, firm characteristics, and industry environment characteristics requires a research method that allows for the analysis of multiple interaction effects and complex configurations such as a set-theoretic approach based on fuzzy set qualitative comparative analysis (fsQCA). FsQCA allows for multiple pathways to the same outcome (“equifinality”) (Fiss, 2007). The view of causality is different from the view found in single-variable-oriented

Results

In our results tables (Table 2 and Table 3) black circles (“●”) indicate the presence of a condition, white circles (“○”) indicate its absence, and blank spaces mark conditions that may be present or absent. Furthermore, the core conditions are represented by larger symbols than those used for the contributing conditions. We report the consistency and coverage scores for our overall solutions as well as for each configuration. The presented solutions contain multiple configurations and the

Discussion and conclusion

Corporate restructuring research has built up a significant knowledge base of how various environmental factors (Bergh, 1998), firm characteristics (Markides, 1995), and transformation design characteristics (Schönhaar, Pidun, et al., 2014) influence the performance of specific BPR moves. Our fuzzy set theoretic analysis adds to this research and develops a multi-level contingency model (see Fig. 1) that analyzes determinants of BPR success. The methodological advantage of fsQCA to capture the

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