The Influence of Prior Industry Affiliation on Framing in Nascent Industries

A very useful paper from the HBS Working Knowledge about The Influence of Prior Industry Affiliation on Framing in Nascent Industries explores the digital camera market to identify some useful trends in firms entering new markets:

New industries sparked by technological change are characterized by high uncertainty. In this paper we explore how a firm’s conceptualization of products in this context, as reflected by product feature choices, is influenced by prior industry affiliation. We study digital cameras introduced from 1991 to 2006 by firms from three prior industries.

The paper hypothesizes that firms entering new industries tend to continue to behave like the industry from which they originate. A unique perspective and one that can be useful for all of us to understand because the corporate mindset is critical to how products get launched.

We hypothesize and find first, that prior industry experience shapes a set of shared beliefs resulting in similar and concurrent firm behavior; second, that firms notice and imitate the behaviors of firms from the same prior industry; and third, that as firms gain experience with particular features, the influence of prior industry decreases. This study extends previous research on firm entry into new domains by examining heterogeneity in firms’ framing and feature-level entry choices.

Let us dig to see what we can learn…
R&D has to always address uncertainty when developing new products. We have to experiment with product configurations, functions and technologies. However, new industries are even more challenging:

Potential customers have little or no experience with products, and their preferences are therefore unformed and unarticulated. Even basic assumptions about what the product is and how it should be used are subject to debate. Similarly, from a technological perspective, uncertainty exists about the rate of performance improvement of the new technology, how components of a technological system will interact, and whether different technological variants will work at all. Market and technological uncertainty are often compounded by competitive uncertainty as firms grapple with shifting industry boundaries and the convergence of firms from previously distinct domains.

The paper intends to analyze and explain how different firms decide to enter new markets and what drives them to be different from each other (heterogeneity). The digital camera industry studied by the authors is quite appropriate because it was at the confluence of multiple technologies / markets:

…the emergence of consumer digital cameras was characterized by high uncertainty and the entry of firms from three prior industries, photography, computing, and consumer electronics, enabling a comparison of the influence of firm background on decisions about which features a digital camera should include.

This interesting.  Digital cameras needed expertise from many different segments: Image Sensors (semiconductor), Optics, Digital Processing, Displays, User Experience (how a camera takes pictures – forte of vendors such as Nikon), film (Kodak) and consumer electronics (including mobile phones).  Market entrant from each participating industry segment approached the market based on their predispositions:

We find that prior industry affiliation had a significant influence on a firm’s initial framing of the nascent product market. Qualitative data indicate that digital camera product concepts and expected uses varied systematically, ranging from an analog camera substitute (photography firms), to a video system component (consumer electronics firms), to a PC peripheral (computing firms) before converging on a product concept that included elements of all three frames.

Also, different entrants from the same industry focused on similar products (based on their prior belief). However, as participants gained more experience with a particular product, they moved away from behavior corresponding to their previous industry – following a three stage model including an era of ferment, convergence on a dominant design, and an era of incremental change:

Our results suggest that firms from the same prior industry shared similar beliefs about what consumers would value as reflected in their concurrent introduction of features — firms were significantly more likely to introduce a feature, such as optical zoom, to the extent that other firms from the same prior industry entered with the feature in the same year, whereas concurrent entry by firms from different prior industries had no influence. Firms were also likely to imitate the behavior of firms from the same prior industry, as opposed to that of firms from different prior industries in introducing some, but not all features. Finally, we find that as a firm’s experience with a particular feature increased, the influence of prior industry decreased.

The paper suggest that industry level (or at least multi-participant) beliefs are important because they tend to shape the industry and the competitive landscape. Sometimes inability to develop all product features allows new entrants in the market. For example, few firms were able to integrate digital cameras with GPS locations  and provide a new user experiences.  It took Apple to combine a touch screen display with a media player in a mobile phone. In new industries R&D managers lack a detailed understanding of customer preferences (they have not evolved yet) and hence the prior experience becomes even more important. May be we should focus on thematic similarities a bit more to address this competitive weakness in traditional R&D management models. An approach focused on how customer would use the product and its features would help the exploration of thematic similarity (may be we can learn from Steve Jobs) .

Impact of the Corporate Mind-set on New Product Launch

Here is an article that discusses the impact of the corporate mind-set on new product launch and its subsequent  market performance. (Katrin Talke. 2010; Journal of Product Innovation Management). The article divides corporate mindsets into three types: analytical, risk-taking, and aggressive posture.  Also, the product launch is boiled down to three decisions: Set launch objectives, Select target markets and position the product in the new market.Not sure how there are orthogonal or independent of each other, but lets play along for a minute.

A research model with mediating effects is proposed, where the corporate mind-set determines the launch strategy decisions, which in turn impact market performance. The model is tested with data on 113 industrial new products launched in business-to-business markets in Germany using a multiple informant approach. 

The results show that of course, the corporate mindset has a strong impact on launch decisions.  Analytical firms focus on all three launch objectives, risk taking firms focus on the first two and aggressive firms just go…

It is found that while an analytical posture relates to all three strategic launch decisions, risk taking and an aggressive posture have a significant impact on two, respectively one, launch strategy elements. 

So you know as much as I do…

Marketing and Manufacturing involvement in R&D

The paper A Cross-National Comparative Study of Senior Management Policy, Marketing–Manufacturing Involvement, and Innovation Performance in the Journal of Product Innovation Management has some useful data to support many intuitive judgments.

The proposed framework is contingent on the national culture of the country in which product development occurs. Structural equation modeling is used to test the framework with data from a sample of 146 U.S. marketing managers and 185 Japanese marketing managers. 

First takeaway is the senior managers have a huge impact on how cross-organizational collaboration works.

The results suggest that a number of senior management policies are effective in promoting joint involvement between the marketing and manufacturing functions during the innovation process.

Another takeaway is the cultural has a significant impact.  Individual behavior / rewards worked in US when they encouraged the R&D team leader, but not in Japan.  More clarity and structure worked in Japan and not in the USA.

While the use of formal cross-functional integration policies was found to promote marketing–manufacturing involvement both in the United States and Japan, team leader autonomy, team rewards, and job rotation were found to promote marketing involvement in the United States but not in Japan. On the other hand, promoting marketing–manufacturing involvement via goal clarity and promotion of teamwork proved to be effective in Japan.

These findings go very well with the research on how IT helps collaboration between R&D and marketing.

The results have a number of implications for product development practice. Foremost among these is the finding that, despite the fundamental ideological differences separating the marketing and manufacturing functions, senior management policies can enhance the level of marketing–manufacturing involvement, and consequently can improve the likelihood of new product success. The second implication is that the effectiveness of specific senior management policies depends on national culture. Thus, managers wishing to improve the marketing–manufacturing interface should select the policies that match the culture in which the product development project is located.

Optimizing Product Development

The paper Balancing Development Costs and Sales to Optimize the Development Time of Product Line Additions in Journal of Product Innovation Management has some very interesting data for all R&D managers.  It has attempted to quantify and test gut feel R&D portfolio managers use in deciding on how to fund development projects – the results might surprise you.

Development teams often use mental models to simplify development time decision making because a comprehensive empirical assessment of the trade-offs across the metrics of development time, development costs, proficiency in market-entry timing, and new product sales is simply not feasible. Surprisingly, these mental models have not been studied in prior research on the trade-offs among the aforementioned metrics. These mental models are important to consider, however, because they define reality, specify what team members attend to, and guide their decision making.

Clearly, problem facing portfolio mangers is rather large – to balance between schedule, costs, market timing and sales (amongst other objectives).  There are no easy approaches to do this quantitatively and managers have to depend on their intuition.  However, the paper’s analysis shows that there is a significant cost to this simplification.  The analysis is based on a significant dataset (albeit one that might have some geographical / cultural bias as it is all from Netherlands).

This survey-based study uses data from 115 completed NPD projects, all product line additions from manufacturers in The Netherlands, to demonstrate that there is a cost to simplifying decision making. Making development time decisions without taking into account the contingency between development time and proficiency in market-entry timing can be misleading, and using either a sales-maximization or a cost-minimization simplified decision-making model may result in a cost penalty or a sales loss.

The results are surprising, but intuitive.  Instead of maximizing just one dimension, optimal results are obtained when a balance is achieved between several competing objectives:

The results from this study show that the development time that maximizes new product profitability is longer than the time that maximizes new product sales and is shorter than the development time that minimizes development costs.

If one is forced lean towards something, development acceleration to maximize sales with associated increased development costs is better than minimizing development costs by extending the schedule.

Furthermore, the results reveal that the cost penalty of sales maximization is smaller than the sales loss of development costs minimization. An important implication of the results is that, to determine the optimal development time, teams need to distinguish between cost and sales effects of development time reductions.

 I have a feeling that this result may have may have other underlying causes like extending development schedule to reduce costs might demoralize teams or increase defects…

High-Performance Product Management

The article High-Performance Product Management: The Impact of Structure, Process, Competencies, and Role Definition in the Journal of Product Innovation Management is interesting for many reasons.  The least of them is because it provides a very good history of research in product management.  More importantly, it combines qualitative interviews with factor analysis and maximum likelihood estimation to develop and test a model for improving performance in product management.

The paper identifies several key factors that potentially impact product management performance. A set of qualitative interviews is conducted to develop hypotheses related to constructs that may drive product management performance. These hypotheses are used to develop a causal model for product management performance that includes constructs related to roles and responsibilities, organization structure, and marketing processes related to product management. An empirical survey of 198 product managers from a variety of industries is conducted to test the causal model. The results of the causal model suggest that performance of a product management organization is driven by structural barriers in the organization, the quality of marketing processes, roles and responsibilities, and knowledge and competencies. The findings suggest that structural boundaries and interfaces are the biggest impediment to effective product management, followed by clarity of roles and responsibilities. The research highlights the importance of organization structure and effective human resource practices in improving product management performance.

Below is what I learned from it:

The overall model proposed for Product Management Excellence is:

Overall recommendation from the model and associated analysis are:

  1. Remove Organizational Barriers and Connect Silos: For example, between existing product management vs. new product management residing in different organizations)
  2. Do not expect product managers to learn on the job: Develop and provide formal training to augment knowledge / competencies / soft skills.
  3. Define Authority and Responsibilities Clearly: In many organizations, product managers have no clear authority and vaguely defined roles. This makes is difficult to deliver results.
  4. Allow Product Managers to focus on Strategy & Planning: 42% of product managers surveyed believed that they spent most of their time on tactical activities and coordination. They had no time to make strategic decisions.
  5. Institute Quality Product Management Processes: From Market Requirement Documents to SKU Planning, lack of effective processes increases non-value-added work and reduces effectiveness

Strategic considerations for teaming, alliances and collaborations

Management Science has a cool (at least I think so) paper on Cross-Function and Same-Function Alliances: How Does Alliance Structure Affect the Behavior of Partnering Firms:

Firms collaborate to develop and deliver new products. These collaborations vary in terms of the similarity of the competencies that partnering firms bring to the alliance. In same-function alliances, partnering firms have similar competencies, whereas in cross-function alliances, partners have very different competencies.

This is very important in co-development.  If a company in consumer electronics is co-designing a new device with a PCB manufacturer, the alliance is likely to be same-functional. Good news is that alliance between firms with similar competencies seem to work well (with caveats – see below).

On examining managers’ view of these alliances, we find that, on average, same-function alliances are expected to perform better than cross-function alliances, holding fixed the level of inputs. 

However, if the same consumer electronics firm wanted to work with a new company on wireless power, a brand new technology, the alliance might be cross-functional.  Many R&D managers are apprehensive of collaboration with dissimilar firms.  The paper uses game theory to come up with a very interesting finding that cross-functional collaboration leads to increased investments:

partners in cross-function alliances may invest more in their alliances than those in same-function alliances.

And that multiple partners are not a problem in cross-functional collaboration, but they are if the firms collaborating have similar competencies.  This is very important in Aerospace & Defense world as many government contracts do indeed have several same-functional parters:

It is also often believed that increasing the number of partnering firms is not conducive for collaborative effort. Our analysis shows that this belief is correct for same-function alliances, but not for cross-function alliances. 

Finally, a somewhat straight forward learning – once the firms have learned from each other and become more similar in competency, they do stop investing the way they used to in the cross-functional stage:

We extend our model to consider alliances where firms have an opportunity to learn from their partners and later leverage this knowledge outside the scope of their alliance. Though such learning increases the resources committed by alliance partners in the learning phase, it decreases investment in the subsequent competition and also dampens the overall investment across the two stages. 

Does innovation improve with external collaboration?

The article Effects of Supplier and Customer Integration on Product Innovation and Performance in the Journal of Product Innovation Management has some empirical evidence on impact of co-design and information sharing with suppliers and customers:

After surveying 251 manufacturers in Hong Kong, this study tested the relationships among information sharing, product codevelopment, product innovativeness, and performance with three control variables (i.e., company size, type of industry, and market certainty). 

The findings seem to indicate a direct, positive relationship between supplier and customer integration and product performance. However, there are a couple of key learnings: For brand new product families (that have not yet percolated through the supply-chain), it is much more important to partner with the emerging customer to learn and perfect the product.  On the other hand, for improving existing product lines, it pays to work with suppliers.  Information sharing with existing customers is not that important, but customer intimacy is:

The empirical findings show that product codevelopment with suppliers improves performance, mediated by innovation. However, the sampled firms cannot improve their product innovation by sharing information with their current customers and suppliers as well as codeveloping new products with the customers. If the adoption of supplier and customer integration is not cost free, the findings of this study may suggest firms work on particular supplier and customer integration processes (i.e., product codevelopment with suppliers) to improve their product innovation. The study also suggests that companies codevelop new products only with new customers and lead users instead of current ones for product innovation.

Customer Loyalty driving R&D

Corporate Executive Board has another one of their useful lists: Six Myths of Customer Loyalty. R&D managers probably are a key driver of customer retention and loyalty and three of these myths are relevant to R&D:

Myth 3: Customer Loyalty Efforts Should Focus on What Customers Say is Most Important
Myth 5: Developing Personal Relationships with Customers is the Best Way for Sales to Drive Loyalty
Myth 6: Employees Who Don’t Face Customers Cannot Affect Customer Loyalty

The idea is that one has to balance internal evaluation with voice of the customer.  Customers are becoming more fickle (necessarily – competitive pressure are enormous throughout the ecosystem.  As pointed out in a Forbes article: The New Normal: Your Customer Is In The Driver’s Seat:

“Today’s consumers are more diverse, more inter-connected and more demanding than ever. Their expectations are rising while their propensity to be loyal to companies is declining, so (let’s face it) they are in the driver’s seat. The questions for companies today are then: Are companies orchestrating where consumers go, and are they making the trip pleasant?”

Some key concepts to keep in mind when R&D managers interact with Product Managers or Marketing…

Focus on technology instead of customers if you want breakthroughs

A marketing study performed at University of Illinois at Urbana-Champaign shows that a technology focus is necessary to generate breakthroughs and that a customer focus only brings in incremental improvements.

Groundbreaking ideas spring most from companies that stress technology, rather than customer needs or staying ahead of competitors, according to research that will appear in the Journal of Product Innovation Management.

Firms that focus on their competitors or customers generate more new product suggestions than technology-based companies, the study found. But the ideas typically net only subtle advances, such as the slow evolution of wireless reading devices, rather than breakthroughs similar to the shift from compact discs to music downloads.

These results make sense intuitively.  The key point in my mind is that an organization needs both – breakthroughs and incremental improvements.  In fact, incremental improvements are critical for day-to-day operations of a business.  The need then is to achieve both through a balanced approach to R&D management.  A careful set of portfolio balancing tools are needed that allow organizations to fund breakthrough ideas and incremental improvements.

Using rivalry to spur innovation

Mckinsey Quarterly has an interesting article on how to spur innovation.  Their suggestion is to use rivalry to drive innovation.  As in the case of GE and reverse innovation in India, the underlying thesis is that innovation does not just happen – R&D managers need to actually provide the circumstances to fuel it.

One such approach is to through rivalry as it happened in Renaissance Italy.  The author proposes three approaches to get the rivalry going in modern R&D organizations:

  • Forming teams. Competing teams could come from different divisions, include a diverse array of experts, and take explicitly different approaches to the same problem. After all, there are often many ways (sometimes coming out of different disciplines) to resolve an R&D challenge, and there is often no way of knowing which one is best without trying them out. Moreover, teams can have biases and narrow specializations, making it all the more important to have an explicit diversity of approaches.  
  • Appreciating differences. During the Renaissance, paintings were placed side by side so that viewers could compare and appreciate them and other artists could borrow from them. In the same way, the various solutions that teams develop can be held up next to one another in order to judge them on their relative merits. On many occasions, ideas from one can be integrated into the other. Or a solution that is ultimately passed over can be sent back to the labs for development in new directions. 
  • Conducting “market tests.” Another way to replicate the practice of paragone is to bring designs to an internal jury or group of customers and let them weigh and contrast the different solutions. In some cases, more than one of the products may find customers who appreciate them, just as Renaissance artists each had their own following.

If you have the luxury of setting up competing teams to do the same development, more power to you.  Clearly, the other two suggestions are valid however you decide to evaluate innovation.  Another interesting approach I remember is through “Theme-Based Innovation”  – something I read in an RTEC case study of Coloplast‘s go-to-market strategy.  They define launch slots for products and have a primary and an alternate candidate.  Whichever makes it goes first.  This way, you are not wasting any development resources.