About

Dr. Stephen K. Markham is the Goodnight Distinguished University Professor of Innovation and Entrepreneurship at NC State University, serving as the Executive Director of Innovation and Entrepreneurship. He holds a Ph.D. in Technology Management from Purdue University, an MBA from the University of California, Irvine, and both a B.S. and M.S. in Social Psychology from Brigham Young University. His research focuses on the roles and processes involved in the front-end of technological innovation, including the role of champions in early commercial development, product development processes, portfolio and program management, and the Front End of Innovation and the Valley of Death. Dr. Markham has a distinguished career that includes co-founding and directing the Technology, Entrepreneurship and Commercialization Program (TEC), and serving as past Director of the Center for Innovation Management Studies (CIMS). He has held leadership roles in industry, including Senior Vice President of Global Strategy and Product Portfolio at Toshiba Global Commerce Solutions, and has been involved with BP’s Innovation Board. He consults with numerous corporations and government agencies such as IBM, P&G, NASA, Cisco, and others. An active angel investor and patent holder in biochemistry, Dr. Markham has also served on editorial review boards and has been a prominent figure in professional organizations like the Product Development & Management Association (PDMA). His teaching spans executive education and MBA programs, emphasizing leadership, corporate culture, innovation, and technology commercialization.

Research topics

• Computer Science
• Sociology
• Political Science
• Engineering
• Engineering ethics
• Mathematics education
• Knowledge management
• Pedagogy
• Psychology

Selected publications

• Teaching High Tech Entrepreneurism: Does It Differ From Teaching Entrepreneurism? (And Does It Matter?)

  2020 · 1 citations

  1st authorCorresponding
  • Computer Science
  • Sociology
  • Political Science

  A comparison is made of curricula and teaching of entrepreneurship in business and engineering schools. Based on this comparison, as well as an analysis of the entrepreneurs hip education literature, two primary recommendations are made: 1) for a process -based approach to teaching entrepreneurship; and 2) for greater emphasis on the early stages of the process, especially the value creation associated with the development of the entrepreneurial opportunity. A number of other issues are raised.

  PublisherOA PDFDOI

• Creating a Corporate Entrepreneurial Ecosystem: The Case of Entrepreneurship Education in the RTP, USA

  Journal of Open Innovation Technology Market and Complexity · 2019-08-26 · 25 citations

  articleOpen accessSenior authorCorresponding

  Today’s dynamic and complex environment means that companies are trying to develop entrepreneurial thinking as a competitive advantage. Universities around the world are simultaneously increasing entrepreneurial training across a broad array of majors. However, the entrepreneurial training is not heavily connected to industry needs. This paper focuses on how industry and universities can cooperate to prepare students for corporate entrepreneurial thinking. The research is based on extensive literature, reports, and in-depth interviews with 28 people from various parts of the RTP (Research Triangle Park), including companies, government agencies, and numerous programs at multiple universities. The major finding from this study is that the strength of entrepreneurial education in the regional innovation system reinforces the companies’ open innovation capacity and their performance. To be successful at launching campus-wide entrepreneurship education to increase understanding of corporate entrepreneurship, Corporate Entrepreneurship Education (CEE) must include (1) entrepreneurial leadership; (2) faculty champions; (3) student-focused policies; (4) engagement with the community; and (5) a decentralized, autonomous structure of entrepreneurship programs.

  PublisherOA PDFDOI

• Innovation Training

  Research-Technology Management · 2017-03-03 · 32 citations

  articleSenior author

  OVERVIEW:Investment in innovation training has the potential to help firms create more successful product offerings, but the extent to which companies do innovation training is unknown. Although efforts to optimize formal processes and integrate Agile methods into development have led to more efficient innovation systems, a large skill gap remains that cannot be overcome by tools and processes alone. To explore the extent to which companies are (or are not) engaging in training to address those skill gaps, interviews were conducted with 30 senior R&D managers from Fortune 1000 companies. The results indicate that even though senior managers’ view of innovation success factors is more focused on human capital than in the past, innovation training rarely happens—80 percent of the companies in our sample reported rarely engaging in structured training to build innovation competencies. We offer some recommendations for addressing this gap.

  PublisherDOI

• PDMA Comparative Performance Assessment Study (CPAS): Methods and Future Research Directions

  Journal of Product Innovation Management · 2016-11-17 · 26 citations

  articleSenior author

  Best practices data are critical for managing and researching new product development. For these purposes the PDMA Research Foundation conducted the 2012 PDMA Comparative Performance Assessment Study (CPAS). This article reports the use of the CPAS data among product development researchers and provides a complete description of the data gathering and cleaning process needed to write additional articles using these data, including other articles in this special issue. This article also reports important results of the CPAS data, makes comparisons between the Best and the Rest firms, and makes comparisons among firms by geography, industry, product/technology/market types, and company size. The results offer insights for academics and practitioners to conduct further research and to find potential new product development best practices.

  PublisherDOI

• Unstructured Text Analytics to Support New Product Development Decisions: Unstructured Text Analytics Can Be Used to Extract Information from Big Data to Support Decision Making for New Product Development

  Research-Technology Management · 2015-03-01 · 1 citations

  article1st authorCorresponding

  Big data promises to dramatically alter the business environment, but collecting the data is only the first step. To yield results, the data must be leveraged to support critical decision-making. For structured data--quantitative answers to questions such as how much was bought or sold or what it cost--this is fairly straightforward; many firms already apply structured approaches to big data to support routine operational decisions. But the vast majority of data--as much as 80 percent, by some unsubstantiated counts (Shilakes and Tylman 1998)--is unstructured, consisting of text; making use of this vast body of unstructured data much more difficult. Handling unstructured data requires new decision-making structures and culture, as well as specialized expertise and a disciplined approach to take advantage of the opportunities such data offer. For an extensive review of uses and approaches to big data analytics see MIS Quarterly's recent special issue, Intelligence (Chen, Chiang, and Storey 2012). Unstructured text data can be used to drive decision making in new product development and customer identification. Critical product development decisions can be improved by using unstructured text analytics to convert text into data that can support well-informed product development decisions. We offer an example of such an approach, which can help companies realize one type of value hidden in masses of unstructured data. The Uses of Structured and Unstructured Data Big data in reality consists of two types of data; structured (numerical) and unstructured (textual) (Table 1). Structured data often consists of large sets of numbers, which can be analyzed using a variety of statistical techniques. These techniques reveal patterns that allow decision makers to see what has happened in the past or even what is happening in real time. These analyses, and the patterns they reveal, are essential to operational decisions regarding, for instance, pricing, distribution, or inventory levels. Textual data, especially unstructured textual data, is more difficult to leverage. Although the exact amount of unstructured data available on the web is unknown, it is undeniably huge and includes all the text contained in government sources such as the websites for the Securities and Exchange Commission, Patent and Trademark Office, National Institute of Health, National Science Foundation, and Department of Energy, as well as academic research, business, and financial reports, consultant research results, and many other sources. Unstructured text is also found in social media outlets such as Facebook, blogs, customer complaint logs, and Twitter, as well as news transcripts, magazines and journals, and many other outlets. Unstructured text analytic approaches seek to isolate critical pieces of information from the flow of text. For example, unstructured text analytics can find announcements in local newspaper that a competitor is creating new jobs or building a new facility, or that a customer is expanding operations in a specific region. Just as Voice of the Customer initiatives search for clues to what customers need in interviews, text analysis uncovers customer needs, competitor actions, emerging trends, and other individual pieces of information necessary to inform critical product decisions. Business applications for unstructured text might include, for example: * Finding where customers or competitors are building new facilities by scanning all building permits issued across the country. * Identifying proposed changes in regulations for a compound your company produces by reading all bills and regulations under consideration in both federal and state legislatures. * Assessing the probability of a recall of defective parts by tracking complaints in blogs and other forms of social media. Reliably finding these pieces of information requires gathering vast amounts of unstructured text using tools like Hadoop or high-performance cloud computing (HPCC). …

  Publisher

• Unstructured Text Analytics to Support New Product Development Decisions

  2015-12-28 · 37 citations

  article1st authorCorresponding

  OVERVIEW:This article describes how to use unstructured text analytics to support critical product development decisions. It clarifies the difference between structured and unstructured data and proposes an analytical process for using big data and natural language processing tools for unstructured text analytics to support managerial decision-making. The usefulness of unstructured data for product development decisions is demonstrated in real cases. In particular, we illustrate the use of text analytics to develop services, to find new customers, and to assess new product viability. Results and outcomes are presented, and practical considerations for selecting and adopting text analytic capabilities are examined.

  PublisherDOI

• Firm Resources & Cognition: Setting a Research Agenda

  Academy of Management Proceedings · 2015-01-01 · 2 citations

  articleSenior author

  What is resource cognition or capability cognition? How do managerial attention and cognition differ, in continuous capability development, versus when there is a need to radically shift the entire capability development portfolio? What is the role of agents’ prior knowledge in how firm resources and capabilities are viewed? What are the challenges that managers face and the logical fallacies they engage in when inferring successful capabilities and competent organizational units? This interactive panel symposium is organized into two parts. In the first part, we define resource cognition and examine how resource assessments differ based on whether the firm’s focus is on continuous capability development or on shifting their entire portfolio. In the second half, we drill down on how characteristics of individual decision makers influence resource cognition. We specifically identify the role of agents’ prior knowledge in how firm resources and capabilities are viewed and also address the common fallacies decision makers engage in when identifying firm competences. We conclude the symposium by emphasizing the need for decision makers in firms to reassess their capabilities on a continuous basis to address market opportunities. The proposed symposium brings together renowned scholars, with considerable theoretical knowledge and empirical research experience, which will contribute to the interactive nature of this panel. We propose to set directions for future research and practice on firm capabilities and resource recognition.

  PublisherDOI

• Marriage and Family Therapy in <scp>NPD</scp> Teams: Effects of We‐ness on Knowledge Sharing and Product Performance

  Journal of Product Innovation Management · 2014-06-16 · 18 citations

  article1st authorCorresponding

  Drawing on marriage and family therapy ( MFT ), this paper introduces the concept of we‐ness to new product development ( NPD ). We‐ness is the shared sense of togetherness family members feel toward each other. We apply we‐ness to NPD as the construct through which people share knowledge at the team, between‐team, and between‐organization levels. The results support the hypotheses that we‐ness increases knowledge sharing and that knowledge sharing increases product performance. In this study, we used regressions to analyze the hypotheses. We found that the greater in‐team we‐ness ( H 1, t = 3.786, p = .000), between‐team we‐ness ( H 2, t = 5.411, p = .000), and between‐organization we‐ness ( H 3, t = 2.940, p = .004) activities there were, the more knowledge sharing in NPD . Results also indicate that knowledge sharing is related to better NPD performance. This paper contrasts team and family as the foundation metaphor to organize people engaged in product development. We argue the team metaphor can be counterproductive in settings where difficult decisions must be made. Teams can lead to individual members suppressing their opinions to “help” the team achieve its goal. Members are expected to sacrifice for the good of the team. That can be adaptive when the task is straightforward. The family metaphor suggests that the group sacrifices for the individual. In a family environment, members protect minority opinions, and in cases where complex, ambiguous decisions must be made individual expertise and insight may come from one person. High‐trust family‐like settings can facilitate sharing sensitive information and norms that can be challenged. The family metaphor suggests a more flexible and tolerant approach to new ideas. At the same time, it is recognized that families can have dysfunctions that can detract from performance. Therefore, managers must carefully apply the use of family‐like settings. The importance for family‐like approaches across organizations seems to be more important as technology complexity increases. Between‐team we‐ness was revealed significantly higher in goods manufacturers than service firms in this study. Small companies need to make extra effort to increase between‐team we‐ness. The idea of approaching product development from a family relations perspective opens up new alternatives for managing people in teams, between teams, and even between organizations. MFT tools to address behaviors and individual performance issues increase the number and nature of managerial tools to increase product performance.

  PublisherDOI

• Bridging the valley of death: lessons learned from 14 years of commercialization of technology education

  IEEE Engineering Management Review · 2014-03-01 · 51 citations

  article

  This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

  PublisherDOI

• The Impact of Front‐End Innovation Activities on Product Performance

  Journal of Product Innovation Management · 2013-09-03 · 118 citations

  article1st authorCorresponding

  This paper describes and tests a model of the impact of front‐end innovation activities on product performance. Data were collected from 272 companies to test the hypothesis that front‐end performance impacts new product performance in the marketplace while controlling for new product development ( NPD ) processes and strategy. The data support the hypothesis that front‐end performance favorably and independently impacts overall product success, time to market, market penetration, and financial performance. Front‐end performance is predicted by a set of activities, including: the actual amount of front‐end work done in various areas, specifically marketing, R&amp;D , and concept development; the existence of a front‐end process; the existence of a champion; and agreement on the order of developmental steps in the front end. Front‐end activities are related to front‐end performance, and front‐end performance is related to NPD performance. This relationship highlights the distinction between front‐end activities and standard product development practices and the importance of building competency in the front end. This is the first study that quantifies both the nature and amount of work done in the front end and relates that work to commercial performance. This research empirically demonstrates the distinction between the front‐end and formal stages and gates types of systems. This suggests that the concept of the front end needs it own set of theoretical constructs to adequately describe and predict this categorically different set of activities. While this study demonstrates the difference between front‐end and stage‐gate systems, it does not establish the limits of those activities. From a managerial point of view recognizing that formal development and front‐end activities are different mandates that these activities must be managed differently. In particular, the skills, structures, processes, governance, leadership, performance metrics, and resources must be assessed separately and differently. These findings suggest that firms should actively manage the flow of ideas from the front end into the more formal development programs.

  PublisherDOI

Frequent coauthors

• Lynda Aiman‐Smith

  14 shared

• Angus I. Kingon

  Providence College

  13 shared

• David L. Baumer

  8 shared

• Michael Zapata

  5 shared

• Hyun‐Jung Lee

  London School of Economics and Political Science

  5 shared

• Ted Baker

  3 shared

• Steve H. Barr

  3 shared

• Timothy L. Michaelis

  Northern Illinois University

  3 shared

Education

• Ph.D.

  Purdue University

• Other

  University of California, Irvine

Awards & honors

• David Ross dissertation award
• PDMA International Dissertation competition
• Distinguished Faculty Extension award
• PDMA’s New Product Development Professional Certification Pr…
• PDMA Research Foundation President