Product and Innovation Portfolio Management
Summary and Keywords
In competitive strategy, firms manage two primary (non-financial) portfolios—the product portfolio and the innovation portfolio. Portfolio management involves resource allocation to balance the important tradeoff of risk reduction and upside maximization, with important decisions around the evaluation, prioritization and selection of products and innovation projects. These two portfolios are interdependent in ways that create reinforcing dynamics—the innovation portfolio is the array of potential future products, while the product portfolio both informs innovation strategy and provides inputs to future innovation efforts. Additionally, portfolio management processes operate at two levels, which is reflected in the literature's structure. The first is a micro lens which focuses on management frameworks to boost portfolio performance and success through project-level selection tools. This research has its roots in financial portfolio management, relates closely to research on new product development and marketing product management, and explores the effects of portfolio management decisions on other organizational functions (e.g., operations). The second lens is a macro lens on portfolio management research, which considers the portfolio as a whole and integrates key organizational and competitive concepts such as entry timing, portfolio management resource allocation regimes (e.g., real options reasoning), organizational experience, and the culling of products and projects. This literature aims to set portfolio management as higher level organizational decision-making capability that embodies the growth strategy of the organization. The organizational ability to manage both the product and innovation portfolios connects portfolio management to key strategic organizational capabilities, including ambidexterity and dynamic capabilities, and operationalizes strategic flexibility. We therefore view portfolio management as a source of competitive advantage that supports organizational renewal.
Keywords: portfolio management, innovation portfolio management, product portfolio management, resource allocation, decision-making, real options, organizational ambidexterity, adaption, strategic flexibility, dynamic capabilities
Two Lenses for Research on Portfolio Management
The vast majority of firms are not comprised of a single product, service, or business, but are diversified both within the core industry and (many times) across industries. This suggests that a firm’s ability to manage both current and future (i.e., innovation) product portfolios is a vital part of organizational strategy and the process of portfolio management poses several key challenges to organizational decision-making systems. Portfolio management thus becomes an important vehicle by which companies realize and operationalize their strategic intents, recognizing future opportunities to explore through the development of new products (Danneels, 2002), as well as determining whether their current product portfolio fits their strategic needs and positioning (Macmillan, Hambrick, & Day, 1982). This links portfolio management directly to strategic management and organizational design across multiple levels of the organization. Successful portfolio management is therefore a dynamic capability that fundamentally affects the ability to add, subtract, and reposition products (including services and other activities), linking to innovative performance at both granular (Ernst, 2002) and organizational (Klingebiel & Adner, 2015) levels.
Portfolio management is, essentially, about decision-making for resource allocation, making tradeoffs across businesses, products, and projects to allocate resources ranging from financial capital to organizational knowledge. It calls for highly relevant decisions touching aspects as diverse as technological strategy, strategy deployment, resource prioritization, product and market cannibalization, risk profile, performance control, management of organizational conflicts, process and organizational structure, design, and integration, among others. This variety of elements involving portfolio management characterizes the complex, heterogeneous, and multilevel nature of the research in the field.
This article organizes the main topics across literatures related to portfolio management, identifying concepts, findings, consensus, and research streams while seeking to identify state-of-the-art research on portfolio management. Before beginning, three important conceptual distinctions are necessary. First, one should note that we do not consider either financial portfolio management, nor the broad strategic literature on diversification (cross-unit portfolio management) as part of this article. Second, we deal with two different, but intrinsically interrelated, activities in portfolio management. One is the portfolio management of new product development (NPD) projects (the innovation portfolio). This stream is linked to operations and technology management (i.e., NPD, project management, project valuation, innovation management, uncertainty management, innovation/technology strategies, among others), and concerns the balancing of resources among projects, or even in still pre-developed ideas, that might be conducted to produce a company’s future set of products, dealing, for instance, with investment decisions (Chao & Kavadias, 2008; Cooper, Edgett, & Kleinschmidt, 1999, 2001; Eggers, 2012b). The other activity overlaps with marketing (i.e., product variety, portfolio complexity, brand positioning, product lifecycle, product lines, market performance, among others), focusing on the evaluation of the composition of the current product portfolio, involving decisions about which products should continue to be produced, or culled (Barksdale & Harris, 1982; Day, 1977; Devinney & Stewart, 1988; Fernhaber & Patel, 2012; Lancaster, 1990; Sorenson, 2000; Tolonen, Shahmarichatghieh, Harkonen, & Haapasalo, 2015).
The firm’s innovation and product portfolios are linked through portfolio management processes (Mikkola, 2001) and strategic management issues (Fernhaber & Patel, 2012; Helfat & Raubitschek, 2000), as well as the link between the technologies delivered by innovation portfolios and the composition of the current product portfolio in responding to industry changes (Capon & Glazer, 1987). For instance, building on the link between innovation and product portfolios, the portfolio management literature worries about the breadth and depth of the portfolio. From the innovation portfolio side, this involves resource allocation across different projects (Barnett, 2008) for portfolio breadth and depth, or the number of different features a firm develops (Klingebiel & Joseph, 2015). From the product portfolio side, this involves positioning in different markets or product lines (Bordley, 2003; Sorescu, Chandy, & Prabhu, 2003). Importantly, product portfolio decisions consider information from the innovation portfolio to make decisions on current products (pipeline decisions), while innovation portfolio decisions include information about the current product portfolio to target and evaluate development projects (e.g., Blau, Pekny, Varma, & Bunch, 2004; McNally, Durmuşoǧlu, & Calantone, 2013).
The third distinction involves the divergence between micro and macro approaches to research on portfolio management. The micro level focuses largely on the study of project- and process-level issues, exploring how management optimizes innovation outcomes in NPD (Cooper et al., 1999; Killen, Hunt, & Kleinschmidt, 2008; Kock, Heising, & Gemunden, 2014), or in deciding on the composition of the current portfolio (Devinney & Stewart, 1988; Tolonen et al., 2015). The macro-level perspective focuses largely on strategic, organizational, and political aspects that influence the effective portfolio management of both the innovation (Eggers, 2012b; Klingebiel & Rammer, 2014; McGrath & Nerkar, 2004) and product (Fernhaber & Patel, 2012; Macmillan et al., 1982; Montgomery, 1985) portfolios.
Finally, we seek to link portfolio management theory to organizational capabilities and strategies. In this vein, there are important issues related to the role of portfolio management in the context of technological adaption and strategic flexibility (Eggers & Park, 2018) and organizational learning (Eggers, 2012a), and in the interface between portfolio management and organizational ambidexterity (Benner & Tushman, 2003; Lavie, Stettner, & Tushman, 2010; March, 1991). Successful portfolio management is a managerial mechanism by which companies balance and accommodate the efforts in exploiting their established viable resource basis and attempts to explore new sources of value creation. This balance permeates portfolio management decision-making and has direct effects on the firm’s ability to pursue radical and incremental innovations concomitantly (Chandrasekaran, Linderman, & Schroeder, 2015; Chao & Kavadias, 2008). Extending this debate, we characterize portfolio management as a dynamic capability (for dynamic capability, see the extensive review of Wilden, Devinney, & Dowling, 2016), able to support a firm’s competitive advantage and resource base renewal (Helfat & Raubitschek, 2000; Killen, Jugdev, Drouin, & Petit, 2012; Sicotte, Drouin, & Delerue, 2014) by altering the established portfolio being offered by the firm.
The Micro Lens on Portfolio Management
The micro-level lens on portfolio management deals with process aspects in managing decision-making for the product and innovation portfolios. This stream concerns both the management of projects being conducted and managerial systems articulated to manage them. Thus, innovation and product portfolio management at the micro level could be defined as a set of dynamic decision-making processes through which firms evaluate and decide on the products and innovation projects they pursue, vis-à-vis each other, in order to compose an optimized portfolio able to sustain their current strategy and future competitive advantage.
At the micro-level perspective, the portfolio management literature is highly influenced by financial portfolio theory (Markowitz, 1952). The heart of financial portfolio theory involves the diversification of risk—by building a diverse enough portfolio of investments with uncorrelated returns, the investor can eliminate non-systemic or firm-specific risk and increase portfolio-level returns (Lintner, 1965). This guiding principle of reducing risk through holding a diverse portfolio in order to maximize return was exported to both product and innovation portfolio management research. First, this informs the logic of composing portfolios aiming to build hedged combinations, balancing risk and return levels. Second, this principle implies that, as financial assets, firms should seek to measure and quantify the risk and return of their product and innovation portfolios using historical data (e.g., Blau et al., 2004).
As a consequence, established portfolio management performance and success parameters (which are basic metrics used to measure the antecedents and consequences of portfolio management) in the micro-level perspective are embedded within financial reasoning. Notably, these established parameters are: (a) strategic alignment, regarding to which extent the portfolio composition translates overall firm’s strategic intents; (b) maximization of global portfolio value, an efficiency relation between resource input and output; and (c) balance: being the proportion in the split of resources according some given criteria, for instance, long- and short-term, high- and low-risk types of products or projects, among others (Cooper et al., 2001; Kester, Hultink, & Griffin, 2014).
These parameters link to two overlaid objectives. First, mainly driven by normative and benchmarking research, this research stream focuses extensively on providing insight for practitioners in structuring portfolio management decision-making systems by presenting tools, managerial practices, and frameworks to be adopted (Cooper, Edgett, & Kleinschmidt, 1997; Griffin, 1997; Killen et al., 2008; Mikkola, 2001; Tolonen et al., 2015). This research largely seeks to build tools for portfolio management, helping firms optimize their decisions.
Second, both descriptive and prescriptive research has sought to understand when firms do and should choose between different tools or approaches (Kester et al., 2014), both building and testing theory (for a review on innovation portfolio management, see Meifort, 2015). Research, for instance, has analyzed the overall effects of using different portfolio management processes in innovation and product performance (Cooper et al., 1999; Jugend, da Silva, Salgado, & Miguel, 2016), including for service-based products (Killen et al., 2008). Other research has sought to identify the styles of portfolio management decision-making across firms (Kester, Hultink, & Lauche, 2009), and the relationship with performance outcomes (Kester et al., 2014). Main findings in this stream indicate that the formal use of portfolio management methods in defined processes increases performance, and that firms prefer to use financial-based frameworks despite no evidence that these frameworks increase innovation performance (Cooper, 2013; Cooper et al., 1999; Killen et al., 2008).
Other research in this vein includes studies indicating that: distinct portfolio management approaches offer different legitimacy mechanisms in the organization through which managers deal with portfolio management decisions (Gutiérrez & Magnusson, 2014); environmental complexity shifts portfolios toward radical innovation and environmental instability toward incremental innovation (Chao & Kavadias, 2008); funding incentives alter the decision in allocating resources among different types of projects, for example that variable funding shifts portfolios toward incremental innovation (Chao, Kavadias, & Gaimon, 2009); portfolio management effectiveness parameters (i.e., portfolio mindset, focus, agility) are associated with the achievement of portfolio success parameters (Kester, Griffin, Hultink, & Lauche, 2011); the formalization of portfolio management processes has an effect in portfolio management success (e.g., Kock & Georg Gemunden, 2016; Kock et al., 2014; Teller, Unger, Kock, & Gemünden, 2012); uncertainties are shaped and addressed in portfolio management by the use of practices, but to build a complete capability, rational mechanisms need to be complemented with other structural and cultural mechanisms (Martinsuo, Korhonen, & Laine, 2014); the organizational structures and processes affect effective decision-making on the product portfolio complexity (PPC) (Closs, Jacobs, Swink, & Webb, 2008).
A core distinction in the macro-portfolio management literature is between breadth (the range of products or innovations included) and depth (the level of specialization within each domain) within the portfolio. While this is not a central distinction in the micro literature, its relevance has begun to appear more over time. In defining strategic arenas (regarding product lines, technologies, markets, levels of risk) in which development projects and products should fit, the firm implicitly maps out where the portfolio should be situated, as well as defining the balance of resources and attention dedicated to each of these arenas. This means the firm implicitly sets the limits of exploration in each domain. From the NPD side, this implies that portfolio management processes (which support decisions on project selection, prioritization, and resource allocation) search for economies of scale in evaluating several different initiatives, facilitating the establishment of standardized processes that reflect strategically defined breadth and depth degrees (Cooper et al., 2001). In addition, managing different projects in an innovation portfolio requires different technical, organizational, market, and resource competences, involving different levels of uncertainty and complexity, shaping and challenging the current capability stretching—“the degree to which an organization extends its technological capabilities to bridge the gap between what it has already known and what the development of a new product requires it to know” (Wang & Chen, 2015, p. 1). This is complicated by the shifts in new products’ development and R&D projects during their execution (Chandrasekaran, Linderman, Sting, & Benner, 2016).
In the product portfolio, breadth and depth imply positioning and comparing the current products being sold with each other (“too similar” or “too different products”), as well as the relationship between products and the range of markets, technologies, and product lines covered (Bordley, 2003). In this context, breadth relates to different product lines (or number of different groups of products), while depth relates to the number of products (and product diversity) in each line. This creates challenges for product portfolio management. For instance, difficulties appear in accessing financial (and correspondent antecedents of risk and return) evaluations of the products and the link between a product’s financial performance and a firm’s performance, with data showing that a large product portfolio drives the last (Kang & Montoya, 2014). Also, there are issues in coping with PPC, considering component technical commonalities and modularity (Jacobs & Swink, 2011), and for the development of algorithms and heuristics to define the optimum mix of products given certain attributes and desired markets (e.g., Jiao, Zhang, & Wang, 2007). Thus, product portfolio decisions (though inherently marketing-related) directly influence production and operations management. Increasing the variety and complexity of the product portfolio affects operational efficiency and performance, challenging production standards, inventory, quality levels, and other manufacturing aspects (Bordley, 2003; Closs et al., 2008), as well as inputs for NPD and innovation portfolio management (for a detailed decision-making model linking current product portfolio and innovation portfolio at the micro level, see Kavadias & Chao, 2007; Loch & Kavadias, 2002).
Portfolio Management Frameworks
Micro-portfolio management research has been largely focused in managerial or theoretical decision-making frameworks, both when discussing best practices and when analyzing performance antecedents and the consequences of portfolio management. Thus, the understanding of portfolio management as a set of dynamic decision-making processes, by which firms decide on the projects they pursue and the products they keep—and stop—selling, drives the development of frameworks to support the design and control of these processes. We now consider the ways in which scholars have proscribed management of both innovation and product portfolios.
Innovation Portfolio Frameworks
Innovation portfolio management has its roots in the literature of NPD. Stage-gate-based processes and approaches for NPD at the project level (e.g., Cooper, 2008), dedicated to managing a single innovation project, defining phases and gates, decision-making criteria, and information flows, orient the development of decision-making processes and approaches for managing a portfolio of innovation projects. Therefore, the NPD process and innovation portfolio management “are two sides of the same coin” (Meifort, 2015, p. 265), which implies that assumptions driving the development of innovation portfolio management frameworks are (at least to some extent) shared with NPD processes. These assumptions, for instance, relate to modeling decision-making processes assuring that project uncertainties are reduced over time (Cooper, 2008), that the firm does not carry projects that should be killed and identifies as early as possible that this action is needed (Lechler & Thomas, 2014), or that there is a clear governance and design for the innovation committees (Sethi & Iqbal, 2008) responsible for evaluating innovation portfolios. As a consequence of the link between NPD and innovation management, the literature borders of the topics are blurred. For example, when discussing decision-making in innovation committees, it is frequently considered that the committees deciding on a single NPD project are also analyzing the innovation portfolio, with portfolio decisions overlapping single project decisions (e.g., Cooper et al., 2000). However, despite the origin of innovation portfolio management frameworks in NPD processes, there are still issues that need to be better understood in the relationship between both, especially regarding their common antecedents and performance effects, as well as related to their integration in a multilevel perspective (Meifort, 2015). In this sense, for example, is good management in NPD also good management for the innovation portfolio? How should decisions on a single project be linked to decisions on an entire portfolio in an integrated way?
In this context, innovation portfolio management frameworks have been designed to optimize decisions on which NPD projects to select, improve, prioritize, kill, deaccelerate, in order to achieve better portfolio strategic alignment, value maximization, and balancing (Cooper et al., 1999; Kester et al., 2014). Researchers have sought to identify, develop, and analyze different tools to evaluate (and value) NPD projects, in order to provide innovation committees with aggregated information about the projects, allowing comparison and consequent decision-making. Cooper et al. (1999, 1997) list a series of methods and practices used by firms to achieve this goal, such as: financial models and financial indices, probabilistic financial models, options pricing theory, strategic approaches scoring models and checklists, analytical hierarchy approaches, behavioral approaches, mapping approaches, or risk–return bubble diagrams. Mikkola (2001) presents an R&D portfolio matrix linking the firm’s competitive advantages and the benefits of the projects. Terwiesch and Ulrich (2008), in turn, relate several of these methods to different phases in managing the opportunity portfolio (considered as the group of future projects to fill firm’s current competitive gaps). Mathews (2011) focuses on financially modeled early-stage ideas in order to allow their evaluation to compose the innovation project portfolio. In a higher level of analysis (of the product line and NPD programs), Loch and Kavadias (2002) propose an algorithm to define resource allocation in R&D portfolios based on marginal returns.
Beyond the many frameworks, micro research on innovation portfolio management has focused on two other issues. The first regards innovation project valuation and directly responds to the portfolio value maximization performance parameter. As incumbent firms typically evaluate opportunities on a financial basis (Christensen, 1997) and financial metrics are objective and clear in communicating risk and return relationships, much has been discussed on this. Beyond the traditional project valuation methods, such as net present value (NPV), return on investment (ROI), and the like, financial reasoning also influences other types of methods including scoring models and bubble diagrams, even when they assess risk and return qualitatively (Kester et al., 2009). Thus, innovation portfolio management established frameworks highly dependent on financial assessments, with data showing the importance companies dedicated to this (Cooper et al., 1999).
As consequence, a problem faced by innovation portfolio managers is how to evaluate innovation projects with high uncertainty, dealing with really innovative initiatives, for which data and information needed to financial modeling is poor or not even available (De Meyer, Loch, & Pich, 2002; Pich, Loch, & de Meyer, 2002). This issue stimulated the theoretical development of sophisticated financial modeling to support the valuation of projects under uncertainty and therefore make their management possible in the portfolio management frameworks, by enabling comparison between projects in the same basis. Options theory pricing applied to project valuation—real options valuation (Dixit & Pindyck, 1995; Myers, 1977; Trigeorgis, 1996), derived from financial options theory (Black & Scholes, 1973; Cox, Ross, & Rubinstein, 1979), would represent a sound solution (Perlitz, Peske, & Schrank, 1999). Real options valuation techniques were adapted to the NPD environment in order to cope with uncertainty and to improve the quantification of managerial flexibility (the possibility of changing projects’ routes during their execution and altering the innovation portfolio composition), increasing the value and competitiveness of more innovative and uncertain projects (Huchzermeier & Loch, 2001; Santiago & Bifano, 2005; Santiago & Vakili, 2005; Wang, Wang, & Wu, 2015). This would allow the composition of a more auspicious innovation portfolio and a higher tolerance to risk. The innovation management literature implicitly applies financial metrics in methods and frameworks, and continually searches for more trustworthy and sophisticated financial tools to evaluate NPD projects and allow their management in the portfolio. However, the same literature points out that firms using financial metrics to manage portfolios have worse innovation performance. Further, that literature indicates that financially driven portfolio management has increased incrementalism of the portfolios, diminishing exposure to more innovative projects (Chao & Kavadias, 2008; Cooper, 2013; Kester et al., 2011).
The second issue highlighted as a concern by micro-innovation portfolio management frameworks relates to the managerial mechanisms to correctly balance portfolios, linking the balancing of innovation portfolios with strategic alignment (Terwiesch & Ulrich, 2008). By defining types of innovation projects to pursue and allocating resources to each group, the rules to evaluate different groups of projects and the decision-making processes around those rules seek to guarantee the correct resource allocation. Thus, firms are organizing how their innovation strategy should be operationalized, as well as linking strategy formulation and implementation (Chao & Kavadias, 2013; Kavadias & Chao, 2007). This gives rise to the key concept of strategic buckets. By this portfolio management framework, after classifying and segmenting innovation projects according to some criteria (typically radical versus incremental, but also including long vs. short term, technological basis, product or market line, uncertainty level), firms define the amount of resources to each of them, as well as the governance for each group (Cooper et al., 2000; Terwiesch & Ulrich, 2008). This approach would allow independent and separate evaluation of different projects, avoiding the influence of idiosyncrasies and preventing resources designed for one type of strategic initiative flow to another during the decision-making process—it would be especially important to protect radical innovation projects, which are more uncertain, with risky and unpredictable returns possibly realized just in the long term, and, because of this, which can be hampered more easily during resource allocation processes (Chao & Kavadias, 2008).
Product Portfolio Management Frameworks
Product portfolio management at the micro level mainly builds on research on product and market analysis, but also includes the consequences of product decisions for other organizational functions, such as operations, supply chain, innovation, and engineering. Notably, product lifecycle analysis and product management matrices (Barksdale & Harris, 1982; Day, 1977; Macmillan et al., 1982), such as the Boston Consulting Group Matrix and the GE Matrix are key examples. From analyzing the market and sales positioning of the current products being carried in the portfolio, the main objective of product portfolio management is determining the optimum mix of products, controlling the breadth and depth of products, and balancing different product arenas and lines (Bordley, 2003) while considering risk exposure, potential return, and strategic intents (Devinney & Stewart, 1988).
While not done as explicitly as in micro-level innovation portfolio management research, which clearly highlights and measures the established portfolio management performance and success parameters (i.e., strategic alignment, portfolio value maximization, balancing), micro-level research on product portfolio management also includes analogous measures and tools. First, research has long worried about the optimal balance among current products in terms of risk and returns (including sophisticated operations research techniques, e.g., Jiao et al., 2007). As before, these typically involve products being understood as financial assets: “the concept of a portfolio of products should imply nothing more than the fact that products are investments and should be treated as such” (Devinney & Stewart, 1988, p. 1082). Clearly, the underlying principles of portfolio value maximization play a key role in these discussions. Second, principles of strategic alignment and balancing are also clearly visible—for example, when classifying products in different quadrants according to market growth and market share (Day, 1977). Controlling the portfolio by lifecycle stage, by relating sales evolution with entry and exit time (Barksdale & Harris, 1982) to decide on keeping or culling a product (basic principles of product portfolio analysis), is nothing more than a typical balancing and allocating effort, with attention and resources in defined buckets of products framed by strategy. Product-related policies and marketing decision-making processes, as known, play important roles in the implementation of business strategies (Walker & Ruekert, 1987).
The concepts of variety and complexity (related to breadth and depth of the portfolios) are also important for product portfolio literature. This balance between variety and complexity is an issue for micro-level literature to the extent that—in contrast to macro-level interest in a broader breadth and depth understanding—this research deals with decisions on the product portfolio with regard to more detailed product and market characteristics. Aspects as product component commonalities, same product variants (not just a mix of different products), and feature sets (Closs et al., 2008; Jacobs & Swink, 2011), or specific market maturity for each product (Kang & Montoya, 2014), represent examples of lower level constructs to be taken in account in product portfolio management.
From this theoretical basis, the micro-level product portfolio literature has focused on improving established product analysis by refinements and increasing assertiveness. For instance, research has explored the key performance indicators (KPIs) for each product lifecycle phase to better support product introduction and culling—portfolio renew (Tolonen et al., 2015). Some limited research has explored the impacts of product portfolio decisions on other organizational functions. Operations, production, and supply chain management receive highly important inputs from product portfolio decisions, which alter their planning, current processes, and established managerial capabilities. Product portfolio frameworks can be focused on: optimizing product lines restructuring costs after product decisions (Shunko et al., 2018); modeling how managers deal with PPC to avoid a decrease in operational performance (Jacobs & Swink, 2011), or the competencies needed to manage such complexities, as well as their relationship with profitability (Closs et al., 2008); developing models to optimally decide on a product portfolio to coordinate operations and finance management (involving inventory, procurement, suppliers, etc.), considering product lifecycle stages (Seifert, Tancrez, & Biçer, 2016); or even theorizing on the coordination burdens in increasing sourcing complexity due to higher product variety (Zhou & Wan, 2017).
As expected, innovation and product portfolio frameworks intertwine in several ways. First, the general, innovation, and product strategies of firms drive both perspectives (Barksdale & Harris, 1982; Meskendahl, 2010), influencing how decision-making processes are structured, in which arenas to play, and establishing portfolio management as a channel to operationalize strategic intents. Second, portfolio management processes and frameworks mix product and innovation portfolio analysis in a joint way, in order to balance the culling of current products and introduce developed new products (Devinney & Stewart, 1988; Loch & Kavadias, 2002; Mikkola, 2001). Third, information on the current product portfolio is used as input in innovation portfolio committees and to frame innovation portfolio decision criteria (Cooper et al., 2001), while also guiding front-end activities (e.g., ideation) in NPD processes, and helping to generate the pool of possible ideas to compose future innovation portfolios (Mathews, 2012). Fourth, concerns around cannibalization (Srinivasan, Ramakrishnan, & Grasman, 2005) in products (products targeting the same costumers) and innovation (projects covering the same market and technological arenas) portfolios also appears in both. Ideally, innovation portfolios should fill and extend opportunities not covered by current products (Terwiesch & Ulrich, 2008), considering current product performance and how new products offered by the innovation portfolio alters the firm’s current product portfolio variety and complexity.
The Macro Lens on Portfolio Management
Portfolio management research at the macro-level perspective deals with the organizational, political, strategic, and environmental aspects of decision-making on product and innovation portfolios. Product and innovation portfolio management at the macro level thus focus on the strategic resource allocation decision-making dynamics intending to frame and potentialize strategic flexibility, competitive advantage, and firm performance. This research links the strategic portfolio of the firm with investment policies, resource allocation regimes, technology, and market entry/exit timing, and their consequent influence on a firm’s organizational capabilities. While the micro-level perspective tends to regards decision-making as a concrete management process, analyzing specific products and NPD projects, the macro-level lens sees portfolio management as an organizational decision-making process.
The macro research on portfolio management frequently employs other organizational theories—the resource-based view (e.g., Kang & Montoya, 2014), organizational learning and adaption (e.g., Eggers, 2012a), the attention-based view (e.g., Barnett, 2008), absorptive capacity and ambidexterity (e.g., Zhou & Wan, 2017), organizational (and dynamic) capabilities (e.g., Lee, 2008; Rothaermel, Hitt, & Jobe, 2006), evolutionary economics (e.g., Sorenson, 2000), and elasticity of demand and supply (e.g., Lancaster, 1990). All these theoretical bases are used to comprehend how organizations frame resource allocation decision-making, and, from the innovation and product portfolio management perspectives, understand the effects of different resource allocation strategies on firm performance (e.g., Klingebiel & Adner, 2015; Sorenson, 2000) and evolution. Strategic investments are the essence of portfolio management. Similar to corporate portfolio management, related to a firm’s diversification in different multi-business (Hedley, 1977; Nippa, Pidun, & Rubner, 2011), innovation and current product portfolio management are embedded in the firm’s strategic and innovation intents and shaped not just by the amount of resources to be allocated in each strategic investment, but by the investment reasoning (the allocation logic and form) implicit in the allocation processes, as well as the timing of those investments.
While micro-level research streams on innovation and product portfolio management were somewhat disconnected (residing in operations and marketing, respectively), at the macro level the two are highly interconnected. Both take a portfolio-based approach to firm performance, which allows for the intuition of innovation portfolios as the renewal stage for product portfolios—two phases of the same “box.” Resource allocation decisions on innovation portfolios, thus, would be framed by product portfolio response, for instance, to industry barrier conditions (Putsis & Bayus, 2001), and influenced by firm’s internal capabilities (De Figueiredo & Kyle, 2006), such as NPD experience (Eggers, 2012a), or strategic portfolio resource allocation processes (Klingebiel & Rammer, 2014).
Instead of distinguishing between innovation and product portfolios, in this section we organize our overview based on three central concepts framing macro-level portfolio management. First, entry and exit timing involves choices of when to access or abandon some market or technological arena (Klingebiel & Joseph, 2015; Suarez, Grodal, & Gotsopoulos, 2015) and the consequences for product and innovation portfolios. Second, a central topic in this literature involves the breadth and depth of the portfolios, meaning how a firm’s innovation and product strategy covers the range of industry, market, technologies, or product lines, and with which degree of specialization (Sorenson, 2000; Theeke, 2016). Third, some research explores the underlying organizational logic driving resource allocation in product and innovation portfolios, considering investment profile and dynamics over time, as well as how the value of risk and uncertainty is perceived (Adner & Levinthal, 2004b; Klingebiel & Adner, 2015; McGrath, Ferrier, & Mendelow, 2004).
Entry and Exit Timing
The relationship between entry/exit timing and innovation/product decisions has long been an interest in literature (e.g., Klepper, 1996) focused on analyzing the link between product strategies, innovation strategies, and industry evolution from birth to maturity. Management scholars have articulated strategies to cope with these challenges (e.g., Suarez et al., 2015) and with the effects on performance (e.g., Bayus & Agarwal, 2007). These concepts have a direct application to portfolio management theories. If portfolio management cares about the current and future (innovation portfolio) mix of products designed to respond to strategic opportunities (based on technological evolution), portfolio management becomes a strategic and managerial mechanism for entries and exits. Simply culling a product and prioritizing one NPD project over another are ways of operationalizing exits, just as approving the development of a specific innovation project may indicate the future entry in some industry, market, or technological arena. The timing of these entries and exits often result in competitive advantages or disadvantages (e.g., Liebermann & Montgomery, 1988).
Portfolio decisions on entry and exit timing are shaped by, among other factors, the firm’s technical and managerial capacities and the coherent resource allocation alignment—for instance, with the alignment of these capacities and the resource allocation increasing new product performance (Hsiao, Chen, Guo, & Hu, 2017) industry barriers and competitive market share status (Putsis & Bayus, 2001) or the firm’s product and innovation capabilities and brand strengthen—as an example, firms with high innovative capacity enter new markets more often and firms with strong brands are likely to enter new markets at low rates (De Figueiredo & Kyle, 2006). A portfolio lens to analyze entry decisions, considering the connection of these decisions with investment strategies, uncertainty levels, and portfolio breadth and selectiveness, supports the understanding of the coordination between timing and portfolio aspects of a firm’s strategy (Klingebiel & Joseph, 2015). At the same time, research has explored the short- and long-term financial impact derived from the interplay between product development strategy (new product introductions) and market entry strategy (timing), and the different capabilities involved in each of these (Kang & Montoya, 2014). From the capability portfolio point of view, the relationship between market entry timing and technological investments (resource allocation in innovation portfolios to build specific technological competences) is still more complicated. In this sense, the investment in an innovation portfolio targeting the consolidation of a technological capability is done before commercialization, when the product portfolio is composed. Moeen (2017), for instance, argues that the required capabilities at the timing of commercial product entry (technical capabilities and complementary assets) differ from those required at the innovative development entry, when integrative capabilities are crucial.
Similarly, portfolio management decisions at the macro level are embedded by dilemmas and problems related to the choice between competing technologies (Eggers, 2014). Once the innovation portfolio (especially those with radical, uncertain, long-term projects) and the product portfolio position the firm in some technological arena, they become critical to facilitate entry and exit strategies. Further, the fungibility of capabilities developed in the innovation portfolio has relevance for success in the product portfolio, which imposes challenges in managing failure, investment flexibility, and resource allocation funding structure during the portfolio management processes (Eggers, 2014).
Portfolio Breadth and Depth
Portfolio breadth and depth are two of the most relevant concepts for understanding macro-portfolio management. Specifically, portfolio breadth captures the range of products in a product line (or innovative projects in an innovation portfolio) that may target different groups of consumers and markets, while depth captures the number of offerings within each category (Bordley, 2003; Sorenson, 2000). This is analogous to product variety, defined as “the number of variants within a specific product group, corresponding broadly to the number of brands as the term is used in marketing literature or the number of models in consumer durable markets” (Lancaster, 1990, p. 189). Grewal, Chakravarty, Ding, and Liechty (2008, p. 262), in turn, use a broader concept, defining portfolio breadth as “the number of different markets targeted” and portfolio depth as the “variation in allocation of resources among different targeted markets.” Fernhaber and Patel (2012), in extension, apply the concept of PPC, arguing that complexity is operationalized by the depth and breadth of the products offered. This articulates specific tensions between breadth and depth, as well as the tradeoffs related to management (e.g., coordination, economies of scale and scope, etc.), and market and performance benefits and costs of having broad or narrow product portfolios (Bordley, 2003; Fernhaber & Patel, 2012; Lancaster, 1990; Sorenson, 2000).
Innovation portfolio management research also applies portfolio breadth and depth concepts, interestingly focusing on understanding how investments in innovation efforts build product portfolio breadth. Current portfolio breadth, then, is a consequence of a firm’s prior resource allocation choices (Barnett, 2008). In a complementary vein, Eggers (2012a) adds learning perspectives on NPD portfolio breadth and depth, linking the breadth of a firm’s experience (how closely related the products the firm has developed are among each other) and the depth of a firm’s experience (number of products developed for a specific niche). The author argues that the breadth of product experience has contingent implications for performance—experience positively impacts performance in new niches and has no effect in the current ones—and that concurrent product breadth reduces performance, while concurrent product depth increases performance. Theeke (2016), in turn, investigates antecedents of innovation breadth, framed as the diversity of elements (in terms of sources of knowledge) used to develop innovations, supporting the idea that inter- and intrafirm competition pressure managers to increase portfolio breadth. Finally, Klingebiel and Rammer (2014, p. 248) directly link portfolio breadth with NPD projects, stating that breadth is “the parallelization of innovation efforts, indicating a strategy of providing initial funding for several different projects” (also applying the term “resource allocation breadth”).
Portfolio Management Resource Allocation
Resource allocation is the heart of portfolio management. Therefore, the decision-making processes and reasoning which shape resource allocation are basic elements for portfolio management effectiveness and consequent innovation, and market and firm performance. More than defining the amount of resources to allocate to keep products in the portfolio or innovation projects in the pipeline, the form—regime or reasoning—through which these resources are allocated is important to understand portfolio management dynamics and outputs.
On one hand, scholars have tried to understand how managers and firms perform selection decisions regarding innovation projects, for instance analyzing the resource allocation mechanisms to fund innovation projects with different levels of novelty and the role of evaluation panels in the organizational selection (e.g., Criscuolo, Dahlander, Grohsjean, & Salter, 2017). On the other hand, they have explored issues beyond the conceptualization of portfolio investment strategies, based on theoretical elements such as: portfolio breadth and depth; portfolio selectiveness (related to discontinue innovation initiatives); innovative intent (how ambitious the innovative efforts are) (Klingebiel & Rammer, 2014); and other dimensions of portfolio resource allocation behavior help to frame resource allocation regimes.
In this direction, Klingebiel and Adner (2015) offer a notable conceptualization. They articulate three allocation behavior dimensions that characterize resource allocation regimes. The first dimension is sequential investment, through which firm allocate resources in a multistep way. The second dimension is low initial commitment to projects, in the case that companies do not compromise all resources at the beginning of the investment, being able to split resource allocation along project execution phases and uncertainty resolution. The third dimension is resource reallocation, related to capacity to dynamically redirect resources from projects with decreased potential to those with more potential. Further, four resource allocation regimes emerge from the combination of these three dimensions: definitive investment, with static bets, in which no dimension is present; definitive investment, with evolving bets, in which is present just the sequencing dimension; investment by tentative, with unstructured bets (counting with sequencing and low initial commitment dimensions); or structured bets (when sequencing, low initial commitment and resource reallocation are present)—the last, characterizing the real options logic reasoning. Due to its described dimensions, especially the characteristics of considering the dynamic resource reallocation between investment initiatives, real options reasoning (ROR) has been an established framework to study strategic investments (Tong & Reuer, 2007), and consequently occupies a relevant role in portfolio management literature (McGrath, 1997; McGrath & Nerkar, 2004).
ROR and Portfolio Management
Real options valuation is designed to facilitate comparisons among projects within a portfolio (e.g., prioritization). The method seeks to deal with project uncertainty, a problem for traditional financial valuation methods, and add the value of managerial flexibility (the possibility to change the project during its execution, including killing it) to NPD project overall value. With the same origin, roots and principles drawn from pricing financial options, ROR is a “systematic strategy framework to evaluate and structure resource investments under uncertainty, and that successful use of real options can lead to the benefits of downside risk reduction and upside potential enhancement” (Tong & Reuer, 2007, p. 4).
An option is “a right, but not an obligation, to take some future specified action at a specified cost” (Trigeorgis & Reuer, 2017, p. 43). Myers (1977) was the first to adapt option pricing theory from financial markets (Black & Scholes, 1973; Cox et al., 1979) to real investments, calling real options “the opportunity to purchase real assets on possibly favorable terms” (Myers, 1977, p. 163), with an option-like behavior. Subsequently, management and strategy scholars have explored ROR as a “way of thinking strategy” offering a guiding framework for many different types of strategic investments (Tong & Reuer, 2007). These initiatives would be understood as investments to build future opportunities. In this sense, investments made today would represent an option in the future, when the firm could decide on whether or not to exercise that option (not continuing to support that strategic investment, for example), in contrast to traditional investment reasoning (based on NPV logic, for example), when investments are not reversible and resources are allocated from the beginning. Similar to financial options, investments would be consolidated after uncertainty resolutions. This managerial flexibility would add value to the strategic initiative and the firm would increase potential gains and limit downside losses (Barnett, 2005).
As discussed by Klingebiel and Adner (2015), the nature of the investments in innovation projects fits with ROR, and research on resource allocation in innovation and R&D investments has long been dedicated to comprehending how ROR could contribute to the management of innovation projects and portfolios (e.g., Kogut & Kalatilaka, 1994; McGrath, 1997; McGrath & Nerkar, 2004). An investment in an innovation initiative today (e.g., the discovery phase of drug development) represents buying an option, and once the project evolves and uncertainties are resolved, a firm can exercise the option by allocating resources in the next investment phase (e.g., scale up the drug production) or not. Taking a broader view, investments to compose the innovation portfolio would be similar to an options portfolio to build a firm’s future technologies, products, business, and, consequently, reframe its competitive advantage.
However, the potential of using ROR in strategic investments, innovation included, is controversial in management research. Despite the theoretical potential of ROR (McGrath et al., 2004), the underlying principles being borrowed from financial options pricing do not necessarily map well to organizational strategy (Adner & Levinthal, 2004a), leaving the fundamental principles of ROR in question. The core of the criticism relates to the fact that, contrary to financial options, the value and time of exercise of real investments are not known when they are acquired, inducing firms to insist on an innovation investment that would need to be interrupted (the abandonment problem) and impeding firms from analyzing the potential upside a priori (Adner & Levinthal, 2004a, 2004b). In addition, organizational issues, such as structural attention and managerial organizational capabilities, would have relevant influence in real options implementation and effective value creation (Barnett, 2008; Tong & Reuer, 2007).
Portfolio Management, Adaptation, and Dynamic Capabilities
At its heart, portfolio management is about the micro- and macro-level processes that allow organizations to control both their current and their potential future product portfolios. When done well, portfolio management allows firms to maximize their existing resources and adapt to future technological change and opportunities. Portfolio management is thus a central dynamic capability that links processes for resource allocation with organizational adaptation. In this section, we outline four specific ways in which these processes are inherently linked—management of cannibalization, reduction of technological uncertainty, strategic flexibility, and ambidexterity.
One of the most significant inertial forces for organizations facing a dynamic environment is the concern about cannibalizing existing successful products (Chandy & Tellis, 1998). While these concerns may sometimes be optimal, in many cases such concerns doom the organization to failure (Henderson, 1993). In order to successfully adapt to a changing environment, organizations must be able to recognize when to cull or cannibalize an existing product by shifting resources toward new products with more potential (Burgelman, 1994). This means that organizations need not just to optimize their current product portfolio or their innovation portfolio independently, but interdependently across both opportunity sets. The level of commitment in resource allocation, represented in portfolio management by how “definitive” and stable the investment in an innovation initiative is, or by the propensity to culling products or not, have high importance in determining the response to technological change. Therefore, for a firm, “strong commitments to existing technologies and the willingness to cannibalize existing products” has the implication of decreasing “incentives to acquire new knowledge or assets, . . . the ability to assimilate new knowledge or assets, . . . the ability to reconfigure its business,” and “make it less likely that a firm possesses knowledge or complementary assets relevant for a new technology” (Eggers & Park, 2018, p. 363). One of the primary opportunities for future research around portfolio management involves more closely linking both product and innovation portfolio management capabilities to better understand how and why some firms are more able to shift resources between portfolios.
Managing the innovation portfolio is essentially about attempts to quantify, reduce, and manage the inherent uncertainty associated with new products and new technologies. Because of the varied nature of the opportunity space, limited organizational resources, and dynamic uncertainty, portfolio management means building resource allocation processes prepared to deal with dynamic resource allocation (and reallocation), considering sequenced investments and low initial commitment (Klingebiel & Adner, 2015), to better support adequate entry and exit timing in markets and technological arenas that will allow firms to implement strategies with the potential to create competitive advantage (Klingebiel & Joseph, 2015). The organization’s goals include managing the tradeoffs between adequate portfolio complexity (breadth and depth) to meet the demands of a dynamic and uncertain world (Klingebiel & Rammer, 2014) with the limited ability to add too much complexity at one time to the firm’s portfolio (Eggers, 2012a). Success in this domain requires decisions around a parallel search for solutions down similar paths (Girotra, Terwiesch, & Ulrich, 2007; Nelson, 1961), an understanding of how to recognize failure and use that motivation to switch to a better alternative (Eggers, 2014; Guler, 2007), and the ability to respond to both upstream and downstream feedback on opportunities while not myopically following one or the other (Christensen & Bower, 1996). Thus, organizational capabilities around project-level resource allocation with intelligent processes to assess and reassess existing projects is central to a firm’s ability to react appropriately to a dynamic opportunity space.
The core of strategic flexibility in organizations is about both the ability to reconfigure what the firm already has and does (Lavie, 2006; Sirmon, Hitt, Ireland, & Gilbert, 2011), and the ability to add elements that the firm needs to be successful. The latter is the domain of portfolio management, and the more micro perspectives on portfolio management offer clear building blocks for this type of strategic flexibility. First, portfolio management is a mechanism of strategy implementation operationalization (Cooper et al., 1999, 2001), translating in the mix projects and products both the deliberate and the emergent strategies (Kopmann, Kock, Killen, & Gemünden, 2017). It is clear that portfolio management involves both a planned component and an improvisational component as uncertainty is resolved, conditions change, and new opportunities become available. The details of how portfolio management should be implemented within firms provides a clear grounding in the details of flexibility. Second, portfolio management supports the building of organizational capabilities, learning processes, and basis of resources that will moderate firms’ ability (and capacity) to perform strategic movements (Brown & Eisenhardt, 1997; Helfat & Raubitschek, 2000). These capabilities emerge through constant reconfiguration of product and innovation portfolios in ways that build capabilities enabling future flexibility (Eggers, 2012a; Zollo & Winter, 2002). These form the core of product- and innovation-based dynamic capabilities. Third, portfolio management defines how flexible resource allocation is to drive the firm to another strategic arena; in the short term, by rethinking the current mix of products (Walker & Ruekert, 1987); and in the long term, by deciding on the innovation portfolio that can lead to future strategy changes (Klingebiel & Rammer, 2014). There is an importantly recursive relationship between the product and innovation portfolios—intuitively, the innovation portfolio is the firm’s future new product offering, but the product portfolio provides direction for the firm’s innovative efforts while the capabilities and knowledge developed through the firm’s existing products provide the groundwork for future innovation efforts. When linked properly, this recursive relationship between the portfolios should increase organizational flexibility; when done poorly (when the portfolios are disconnected) flexibility plummets. Future research exploring the interrelationships between the portfolios would dramatically enhance our understanding of the path-dependent nature of organizational evolution.
Finally, the interrelationship between the product (current) and innovation (future) portfolios within the firm highlights the important links between portfolio management and organizational ambidexterity (Andriopoulos & Lewis, 2009; Gibson & Birkinshaw, 2004). Much has been written on the challenges in accommodating exploration and exploitation activities in the same management system (Lavie et al., 2010; March, 1991; Tushman & O’Reilly, 1996), but most of those discussions are relatively high level, without digging into the specific processes and routines that drive the organization’s ability to manage both vital activities. Existing portfolio management research on the processes and resource allocation regimes has been investigating exactly these issues—how to manage innovation projects and products responsible for exploiting current firms’ resource basis (e.g., incremental innovation) and for exploring future opportunities (e.g., radical innovation) (Chao & Kavadias, 2008; McGrath & Nerkar, 2004). This issue permeates all portfolio management decision-making processes (e.g., Chandrasekaran et al., 2015), from micro to macro literature. It goes from differences in accessing the value, risk, and return of innovation projects and products with distinct levels of uncertainty, to macro-resource allocation regimes capable of sustaining current organizational capabilities, and, at the same time of building the future. A signal of the importance of portfolio management to build firms’ future basis of resources by the establishment of processes and routines is the tendency to characterize it as a dynamic capability (e.g., Killen et al., 2012; Sicotte et al., 2014). Building better bridges linking theory on ambidexterity to the specific processes and tools developed to manage portfolios would improve our understanding of how firms manage for ambidexterity.
Overall, this article offers an overview of the research on portfolio management, across a wide array of literatures, and concerning both product and innovation portfolios across micro and macro levels of analysis. Hopefully this provides a guide to orient those interested in portfolio management research. We hope that research in these domains will continue to integrate across different levels of analysis and types of research. Perhaps most importantly, the strong split between the research on product versus innovation portfolios seems especially problematic, as the real opportunity exists around studying the interdependence between the two portfolios and how that interdependence affects the optimal portfolio management strategy. Such a perspective would not only provide more practical and feasible tools for managing portfolios within firms, but would better articulate the important links between portfolio management and key organizational capabilities—adaptation, flexibility, dynamic capabilities, and ambidexterity.
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