new product research paper

• Browse All Articles
• Newsletter Sign-Up

• 21 Apr 2023
• Research & Ideas

The $15 Billion Question: Have Loot Boxes Turned Video Gaming into Gambling?

Critics say loot boxes—major revenue streams for video game companies—entice young players to overspend. Can regulators protect consumers without dampening the thrill of the game? Research by Tomomichi Amano and colleague.

• 28 Mar 2023

The FDA’s Speedy Drug Approvals Are Safe: A Win-Win for Patients and Pharma Innovation

Expediting so-called breakthrough therapies has saved millions of dollars in research time without compromising drug safety or efficacy, says research by Ariel Stern, Amitabh Chandra, and colleagues. Could policymakers harness the approach to bring life-saving treatments to the market faster?

• 31 Jan 2023
• Cold Call Podcast

Addressing Racial Discrimination on Airbnb

For years, Airbnb gave hosts extensive discretion to accept or reject a guest after seeing little more than a name and a picture, believing that eliminating anonymity was the best way for the company to build trust. However, the apartment rental platform failed to track or account for the possibility that this could facilitate discrimination. After research published by Professor Michael Luca and others provided evidence that Black hosts received less in rent than hosts of other races and showed signs of discrimination against guests with African American sounding names, the company had to decide what to do. In the case, “Racial Discrimination on Airbnb,” Luca discusses his research and explores the implication for Airbnb and other platform companies. Should they change the design of the platform to reduce discrimination? And what’s the best way to measure the success of any changes?

• 12 Oct 2022

When Design Enables Discrimination: Learning from Anti-Asian Bias on Airbnb

Airbnb bookings dropped 12 percent more for hosts with Asian names than other hosts during the early months of the COVID-19 pandemic, says research by Michael Luca. Could better design deter bias, particularly during times of crisis?

• 05 May 2022

Why Companies Raise Their Prices: Because They Can

Markups on household items started climbing years before the COVID-19 pandemic. Companies have realized just how much consumers will pay for the brands they love, says research by Alexander MacKay. Closed for comment; 0 Comments.

• 19 Oct 2021

Should Global Beer Company Molson Coors Dive into the Cannabis Beverages Business?

In March 2019, Molson Coors CEO Mark Hunter considered a request to pull forward $65 million to build a facility in Canada to produce cannabis beverages. This request was not part of the original plan to test the waters with a few products in a small geography to see if there was a viable market opportunity, given that there was no legal market yet. It's this change in direction that gives Hunter pause. Should he approve the request, or push the team back to the original, more conservative plan? Senior Lecturer Derek van Bever and Steve Kaufman discuss balancing exploitation versus exploration inside this global brewing company in the case, "Beyond Beer: Brewing Innovation at Molson Coors." Open for comment; 0 Comments.

• 29 Sep 2021

For Entrepreneurs, Blown Deadlines Can Crush Big Ideas

After a successful launch, entrepreneurs struggle to anticipate the complexities of product upgrades, says research by Andy Wu and Aticus Peterson. They offer three tips to help startups avoid disastrous delays. Open for comment; 0 Comments.

• 02 Aug 2020
• What Do You Think?

Is the 'Experimentation Organization' Becoming the Competitive Gold Standard?

SUMMING UP: Digital experimentation is gaining momentum as an everyday habit in many organizations, especially those in high tech, say James Heskett's readers. Open for comment; 0 Comments.

• 28 Jul 2020

Racism and Digital Design: How Online Platforms Can Thwart Discrimination

Poor design decisions contribute to racial discrimination on many online platforms. Michael Luca and colleagues offer tips for reducing the risk, used by Airbnb and other companies. Open for comment; 0 Comments.

• 23 Mar 2020
• Working Paper Summaries

The Effects of Hierarchy on Learning and Performance in Business Experimentation

Do senior managers help or hurt business experiments? Analyzing a dataset of more than 6,300 experiments on the A/B/n testing platform Optimizely, this study suggests that involving senior executives in experimentation teams can have surprising consequences.

• 17 Feb 2020
• Sharpening Your Skills

How Entrepreneurs Can Find the Right Problem to Solve

Identifying a customer's pain points is the first step for entrepreneurs in developing a new product. Julia Austin offers tips for choosing the right "job to be done." Open for comment; 0 Comments.

• 16 Sep 2019

Crowdsourcing Is Helping Hollywood Reduce the Risk of Movie-Making

Hollywood insiders have created "The Black List," which helps surface good but often overlooked scripts. Does the wisdom of the crowd work at the box office? Research by Hong Luo. Open for comment; 0 Comments.

• 19 Dec 2018

Find and Replace: R&D Investment Following the Erosion of Existing Products

This study sheds light on how product outcomes shape the direction of innovation and markets for technology. In the drug development industry in particular, negative product shocks appear to spur investment changes both within the directly affected firm and in competing firms in the same R&D markets.

• 10 Dec 2018

Platform Competition: Betfair and the U.K. Market for Sports Betting

Since the early 2000s, online betting exchanges have had a new relationship with customers relative to traditional bookmakers, providing a platform to match individuals willing to lay and back the same outcome. This study shows how exchanges’ platform design choices have major implications for their likelihood of success.

• 06 Aug 2018

Supersmart Manufacturing Tools are Lowering Prices on TVs, Bulbs, and Solar Panels

Electronics manufacturers are finding it increasingly difficult to stay ahead of low-cost competitors, says Willy Shih. Open for comment; 0 Comments.

• 21 Feb 2018

When a Competitor Abandons the Market, Should You Advance or Retreat?

Companies pay close attention when a competitor drops out of the market, according to new research by Joshua Lev Krieger. Too often, though, they come to the wrong conclusion. Open for comment; 0 Comments.

• 20 Dec 2017
• Lessons from the Classroom

How to Design a Better Customer Experience

With the help of LEGO bricks, Stefan Thomke helps business executives discover how design principles can serve as building blocks to create a great customer experience. Open for comment; 0 Comments.

• 06 Dec 2017

Trials and Terminations: Learning from Competitors' R&D Failures

When companies terminate R&D projects, it has ripple effects on the project selection decisions of rival firms and the broader competitive environment. Examining firm responses to others’ failures, this paper introduces a new model of R&D investment decisions, and empirically investigates when knowledge generated by rivals directly enters specific project investment decisions.

• 29 Mar 2017

The Story of Why Humans Are So Careless With Their Phones

Consumers act more recklessly with the products they own when better versions become available, according to research by Silvia Bellezza, Joshua M. Ackerman and Francesca Gino. This comic by Josh Neufeld explains. Open for comment; 0 Comments.

• 20 Jul 2016

Airplane Design Brings Out the Class Warfare in Us All

Air rage is often blamed on overcrowded flights and postage stamp-size seats, but researchers Michael Norton and Katherine A. DeCelles find another culprit: resentment toward passengers in first class. Open for comment; 0 Comments.

Innovation and New Products Research: A State-of-the-Art Review, Models for Managerial Decision Making, and Future Research Directions

• First Online: 14 July 2017

Cite this chapter

• Tingting Fan 6 ,
• Peter N. Golder 7 &
• Donald R. Lehmann 8  

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 254))

5277 Accesses

5 Citations

This chapter has a three-fold purpose. First, we provide a literature review of major papers in the field of new products research. We organize our review into four tables, one for each of the four stages of the new product development process, and then by topic within each stage. We provide a short summary of each paper in the tables. Second, we highlight specific models within each stage of the new product development process. These models are useful for marketing researchers and managers tackling challenges in the new products domain. Third, after reviewing the literature, we suggest numerous general research directions as well as specific research questions to guide future investigations in this area.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

The Journal of Product Innovation Management is also a valuable repository of new products research.

Aral, Sinan, and Dylan Walker. 2014. Tie strength, embeddedness, and social influence: A large-scale networked experiment. Management Science 60 (6): 1352–1370.

Article   Google Scholar  

Assmus, G. 1975. NEWPROD: The design and implementation of a new product model. Journal of Marketing 39 (1): 16–23.

Bass, Frank M. 1969. A new product growth model for consumer durables. Management Science 15 (5): 215–227.

Bayus, Barry L. 2013. Crowdsourcing new product ideas over time: An analysis of the Dell IdeaStorm community. Management Science 59 (1): 226–244.

Bemmaor, Albert C., and Nicolas Glady. 2012. Modeling purchasing behavior with sudden ‘Death’: A flexible customer lifetime model. Management Science 58 (5): 1012–1021.

Blackburn, J.D., and K.J. Clancy. 1980. LITMUS: A new product planning model. In Proceedings: Market measurement and analysis, ed. Robert P. Leone, 182–193. Providence, R.I.: The Institute of Management Sciences.

Google Scholar  

Blattberg, R., and J. Golanty. 1978. Tracker: An early test market forecasting and diagnostic model for new product planning. Journal of Marketing Research 15 (2): 192–202.

Biyalogorsky, E., W. Boulding, and R. Staelin. 2006. Stuck in the past: Why managers persist with new product failures. Journal of Marketing 10 (2): 108–121.

Bohlmann, Jonathan D, Peter N. Golder, and Debanjan Mitra. 2002. Deconstructing the pioneer’s advantage: Examining vintage effects and consumer valuations of quality and variety. Management Science 48 (9): 1175–1195.

Borah, Abhishek, and Gerard J. Tellis. 2014. Make, buy, or ally? choice of and payoff from announcements of alternate strategies for innovations. Marketing Science 33 (1): 114–133.

Boulding, W., and M. Christen. 2003. Sustainable pioneering advantage? Profit implications of market entry order. Marketing Science 22 (3): 371–392.

Boulding, William, and Markus Christen. 2008. Disentangling pioneering cost advantages and disadvantages. Marketing Science 27 (4): 699–716.

Boulding, W., R. Morgan, and R. Staelin. 1997. Pulling the plug to stop the new product drain. Journal of Marketing Research 34 (1): 164–176.

Bowman, D., and H. Gatignon. 1996. Order of entry as a moderator of the effect of the marketing mix on market share. Marketing Science 15 (3): 222–242.

Boyd, D., and J. Goldenberg. 2013. Inside the box . New York, NY: Simon and Schuster.

Bruce, Norris I., Natasha Zhang Foutz, and Ceren Kolsarici. 2012. Dynamic effectiveness of advertising and word of mouth in sequential distribution of new products. Journal of Marketing Research 49 (4): 469–486.

Burroughs, James E., Darren W. Dahl, C. Page Moreau, Amitava Chattopadhyay, and Gerald J. Gorn. 2011. Facilitating and rewarding creativity during new product development. Journal of Marketing 75 (4): 53–67.

Carpenter, G.S., R. Glazer, and K. Nakamoto. 1994. Meaningful brands from meaningless differentiation: The dependence on irrelevant attributes. Journal of Marketing Research 31 (3): 339–350.

Carpenter, G.S., and K. Nakamoto. 1990. Competitive strategies for late entry into a market with a dominant brand. Management Science 36 (10): 1268–1278. Focused Issue on the State of the Art in Theory and Method in Strategy Research.

Chandy, R.K., and G.J. Tellis. 1998. Organizing for radical product innovation: The overlooked role of willingness to cannibalize. Journal of Marketing Research 35 (4): 474–487.

Chen, Yubo, Qi Wang, and Jinhong Xie. 2011. Online social interactions: A natural experiment on word-of-mouth versus observational learning. Journal of Marketing Research 48 (2): 238–254.

Chevalier, J.A., and D. Mayzlin. 2006. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research 43 (3): 343–354.

Claycamp, H.J., and L.E. Liddy. 1969. Prediction of new product performance: An analytical approach. Journal of Marketing Research 6 (4): 414–420.

Cooper, L.G. 2000. Strategic marketing planning for radically new products. Journal of Marketing 64 (1): 1–16.

Cooper, R.G. 1990. Stage-gate systems: A new tool for managing new products. Business Horizons 33 (3): 44–54.

Cooper, R.G. 1994. Perspective third-generation new product processes. Journal of Product Innovation Management 11 (1): 3–14.

Coviello, Nicole E., and Richard M. Joseph. 2012. Creating major innovations with customers: Insights from small and young technology firms. Journal of Marketing 76 (6): 87–104.

Cui, Anna S., and Gina O’Connor. 2012. Alliance portfolio resource diversity and firm innovation. Journal of Marketing 76 (4): 24–43.

Dahan, E., A.J. Kim, A.W. Lo, T. Poggio, and N. Chan. 2011. Securities trading of concepts (STOC). Journal of Marketing Research 48 (3): 497–517.

Ding, M., and J. Eliashberg. 2002. Structuring the new product development pipeline. Management Science 48 (3): 343–363.

Dotzel, Thomas, Venkatesh Shankar, and Leonard L. Berry. 2013. Service innovativeness and firm value. Journal of Marketing Research 50 (2): 259–276.

Ederer, Florian, and Gustavo Manso. 2013. Is Pay for Performance Detrimental to Innovation? Management Science 59 (7): 1496–1513.

Ehrenberg, A.S.C. 1972. Repeat-buying: Theory and application . Amsterdam: North Holland Press.

Fader, P.S., B.G.S. Hardie, and K.L. Lee. 2005. Counting your customers the easy way: An alternative to the Pareto/NBD model. Marketing Science 24 (Spring): 275–284.

Fader, P.S., and D.C. Schmittlein. 1992. Excess behavioral loyalty for high-share brands: Deviations from the Dirichlet model for repeat purchasing. Journal of Marketing Research 30 (November): 478–493.

Fisher, J.C., and R.H. Pry. 1971. A simple substitution model of technological change. Technological Forecasting and Social Change 3: 75–88.

Fourt, L.A., and J.W. Woodlock. 1960. Early prediction of market success for new grocery products. Journal of Marketing 25 (2): 31–38.

Ganesan, S., A.J. Malter, and A. Rindfleisch. 2005. Does distance still matter? Geographic proximity and new product development. Journal of Marketing 69 (4): 44–60.

Gatignon, H., T.S. Robertson, and A.J. Fein. 1997. Incumbent defense strategies against new product entry. International Journal of Research in Marketing 14 (2): 163–176.

Gielens, Katrijn. 2012. New products: The antidote to private label growth? Journal of Marketing Research 49 (3): 408–423.

Godes, D., and D. Mayzlin. 2004. Using online conversations to study word-of-mouth communication. Marketing Science 23 (4): 545–560.

Goldenberg, Jacob, David Mazursky, and Sorin Solomon. 1999. Toward identifying the templates of new products: A channeled ideation approach. Journal of Marketing Research 36 (2): 200–210.

Goldenberg, J., R. Horowitz, A. Levav, and D. Mazursky. 2003. Finding your innovation sweet spot. Harvard Business Review 81 (3): 120–130.

Goldenberg, Jacob, Donald R. Lehmann, and David Mazursky. 2001. The idea itself and the circumstances of its emergence as predictors of new product success. Management Science 47 (1): 69–84.

Goldenberg, Jacob, Barak Libai, and Eitan Muller. 2002. Riding the saddle: How cross-market communications can create a major slump in sales. Journal of Marketing 66 (2): 1–16.

Golder, Peter N., and Debanjan Mitra (editors). 2017. Handbook of research on new product development . Edward Elgar.

Golder, Peter N., Rachel Shacham, and Debanjan Mitra. 2009. Innovations’ origins: When, by whom, and how are radical innovations developed? Marketing Science 28 (1): 166–179.

Golder, Peter N., and Gerard J. Tellis. 1993. Pioneer advantage: Marketing logic or marketing legend? Journal of Marketing Research 30 (May): 158–170.

Gopinath, Shyam, Pradeep K. Chintagunta, and Sriram Venkataraman. 2013. Blogs, advertising, and local-market movie box office performance. Management Science 59 (12): 2635–2654.

Gopinath, Shyam, Jacquelyn S. Thomas, and Lakshman Krishnamurthi. 2014. Investigating the relationship between the content of online word of mouth, advertising, and brand performance. Marketing Science 33 (2): 241–258.

Green, P.E., J.D. Carroll, and S.M. Goldberg. 1981. A general approach to product design optimization via conjoint analysis. Journal of Marketing 45 (Summer): 17–37.

Green, P.E., and V.R. Rao. 1971. Conjoint measurement for quantifying judgmental data. Journal of Marketing Research 8 (August): 355–363.

Green, P.E., and V. Srinivasan. 1978. Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research 5 (2): 103–123.

Green, P.E., and V. Srinivasan. 1990. Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing 54 (4): 3–19.

Griffin, A. 1997. PDMA research on new product development practices: Updating trends and benchmarking best practices. Journal of Product Innovation Management 4 (6): 429–458.

Griffin, A., and J.R. Hauser. 1993. The voice of the customer. Marketing Science 12 (1): 1–27.

Gupta, Sunil, and Donald R. Lehmann. 2003. Customers as assets. Journal of Interactive Marketing 17 (1): 9–24.

Gupta, Sunil, Donald R. Lehmann, and Jennifer A. Stuart. 2004. Valuing Customers. Journal of Marketing Research 41 (1): 7–18.

Haenlein, Michael, and Barak Libai. 2013. Targeting revenue leaders for a new product. Journal of Marketing 77 (3): 65–80.

Hanssens, Dominique M. 2015. Empirical generalizations about marketing impact , 2nd ed. Cambridge, MA: Marketing Science Institute.

Hauser, J.R., and D. Clausing. 1988. The house of quality. Harvard Business Review 66 (3): 63–73.

Hauser, John R., Guilherme Liberali, and Glen L. Urban. 2014. Website morphing 2.0: switching costs, partial exposure, random exit, and when to morph. Management Science 60 (6): 1594–1616.

Hauser, John R., and S.M. Shugan. 1983. Defensive marketing strategies. Marketing Science 2 (4): 319–360.

Hauser, John R., Gerard J. Tellis, and Abbie Griffin. 2006. Research on innovation: A review and agenda for marketing science. Marketing science 25 (6): 687–717.

Henard, D.H., and D.M. Szymanski. 2001. Why some new products are more successful than others. Journal of Marketing Research 38 (3): 362–375.

Hitsch, G.A. 2006. An empirical model of optimal dynamic product launch and exit under demand uncertainty. Marketing Science 25 (1): 25–50.

Hoeffler, Steve. 2003. Measuring preferences for really new products. Journal of Marketing Research 40 (4): 406–420.

Homburg, Christian, Martin Schwemmle, and Christina Kuehnl. 2015. New product design: Concept, measurement, and consequences. Journal of Marketing 79 (3): 41–56.

Horsky, D., and P. Nelson. 1992. New brand positioning and pricing in an oligopolistic market. Marketing Science 11 (2): 133.

Iyengar, Raghuram, Christophe Van den Bulte, and Thomas W. Valente. 2011. Opinion leadership and social contagion in new product diffusion. Marketing Science 30 (2): 195–21.

Jerath, Kinshuk, Peter S. Fader, and Bruce G.S. Hardie. 2011. New perspectives on customer ‘death’ using a generalization of the Pareto/NBD model. Marketing Science 30 (5): 866–880.

Johnson, R.M. 1974. Tradeoff analysis of consumer value. Journal of Marketing Research 11 (2): 121–127.

Johnson, R.M. 1987. Adaptive conjoint analysis. Paper presented at the Sawtooth Software Conference.

Kalyanaram, G., W.T. Robinson, and G.L. Urban. 1995. Order of market entry: Established empirical generalizations, emerging empirical generalizations, and future research. Marketing Science 14 (3 Supplement): G212–G221.

Kalyanaram, G., and G.L. Urban. 1992. Dynamic effects of the order of entry on market share, trial penetration, and repeat purchases for frequently purchased consumer goods. Marketing Science 11 (3): 235–250.

Kim, Hye-Jin, Young-Hoon Park, Eric T. Bradlow, and Min Ding. 2014. PIE: A holistic preference concept and measurement model. Journal of Marketing Research 51 (3): 335–351.

Kopalle, P., and D.R. Lehmann. 2006. Setting quality expectations when entering a market: What should the promise be? Marketing Science 25 (1): 8–24.

Kornish, Laura J., and Karl T. Ulrich. 2011. Opportunity spaces in innovation: Empirical analysis of large samples of ideas. Management Science 57 (1): 107–128.

Kornish, Laura J., and Karl T. Ulrich. 2014. The importance of the raw idea in innovation: Testing the sow’s ear hypothesis. Journal of Marketing 51 (1): 14–26.

Koukova, Nevena T., P.K. Kannan, and Amna Kirmani. 2012. Multiformat digital products: How design attributes interact with usage situations to determine choice. Journal of Marketing Research 49 (1): 100–114.

Kuehn, A.A. 1962. Consumer brand choice as a learning process. Journal of Advertising Research 2: 10–17.

Landwehr, R., Aparna A. Labroo, and Andreas Herrmann. 2011. Gut liking for the ordinary: Incorporating design fluency improves automobile sales forecasts. Marketing Science 30 (3): 416–429. http://pubsonline.informs.org/action/doSearch?text1=Landwehr%2C+J+R&field1=ContribJan .

Landwehr, Jan R., Daniel Wentzel, and Andreas Herrmann. 2013. Product design for the long run: Consumer responses to typical and atypical designs at different stages of exposure. Journal of Marketing 77 (5): 92–107.

Lehmann, Donald R., and Peter N. Golder. 2014. New products research. In The history of marketing science , ed. Russell S. Winer and Scott A. Neslin, now publishers.

Lilien, G.L., P.D. Morrison, K. Searls, M. Sonnack, and E.V. Hippel. 2002. Performance assessment of the lead user idea-generation process for new product development. Management Science 48 (8): 1042–1059.

Liu, Y. 2006. Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing 70 (3): 74–89.

Liu, Qing, and Yihui Tang. 2015. Construction of heterogeneous conjoint choice designs: A new approach. Marketing Science 34 (3): 346–366.

Luce, D.R., and J.W. Turkey. 1964. Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology 1: 1–27.

Luo, L., P.K. Kannan, and B.T. Ratchford. 2007. New product development under channel acceptance. Marketing Science 26 (2): 149–163.

Luo, Lan, and Olivier Toubia. 2015. Improving online idea generation platforms and customizing the task structure on the basis of consumers’ domain-specific knowledge. Journal of Marketing (forthcoming).

Ma, Zhenfeng, Tripat Gill, and Ying Jiang. 2015. Core versus peripheral innovations: The effect of innovation locus on consumer adoption of new products. Journal of Marketing Research 52 (3): 309–324.

Mahajan, Vijay, Eitan Muller, and Frank M. Bass. 1995. Diffusion of new products: Empirical generalizations and managerial uses. Marketing Science 14 (3): G79–G88.

Massy, W.F. 1969. Forecasting the demand for new convenience products. Journal of Marketing Research 6 (4): 405–412.

McFadden, Daniel. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in econometrics , ed. Zarembka, 105–42. New York: Academic Press.

Min, S., M.U. Kalwani, and W.T. Robinson. 2006. Market pioneer and early follower survival risks: A contingency analysis of really new versus incrementally new product-markets. Journal of Marketing 70 (1): 15–33.

Montoya-Weiss, M.M., and R. Calantone. 1994. Determinants of new product performance: A review and meta-analysis. Journal of Product Innovation Management 11 (5): 397–417.

Moorman, C., and A.S. Miner. 1997. The impact of organizational memory on new product performance and creativity. Journal of Marketing Research 34 (1): 91–106.

Narasimhan, C., and Z.J. Zhang. 2000. Market entry strategy under firm heterogeneity and asymmetric payoffs. Marketing Science 19 (4): 313–325.

Narayanan, Sridhar, and Puneet Manchanda. 2009. Heterogeneous learning and the targeting of marketing communication for new products. Marketing Science 28 (3): 424–441.

Netzer, O. and V. Srinivasan. 2011. Adaptive, self-explication of multiattribute preferences. Journal of Marketing Research 48 (1): 140–156.

Nowlis, S.M., and I. Simonson. 1996. The effect of new product features on brand choice. Journal of Marketing Research 33 (1): 36–46.

Ofek, E., and M. Sarvary. 2003. R&D, marketing and the success of next generation products. Marketing Science 22 (3): 355–370.

Parfitt, J.H., and B.J.K. Collins. 1968. Use of consumer panels for brand-share prediction. Journal of Marketing Research 5 (2): 131–145.

Peres, Renana, Eitan Muller, and Vijay Mahajan. 2010. Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing 27 (2): 91–106.

Pringle, L.G., R.D. Wilson, and E.I. Brody. 1982. News: A decision-oriented model for new product analysis and forecasting. Marketing Science 1 (1): 1–29.

Rao, Vithala R. 2014. Conjoint analysis. In The history of marketing science , ed. Russell S. Winer and Scott A. Neslin. World Scientific - Now Publishers Series in Business.

Rao, V.R. 2008. Developments in conjoint analysis. In Handbook of marketing decision models , 1st ed, ed. Berend Wierenga, 23–53. New York: Springer Science+Business Media.

Chapter   Google Scholar  

Reinartz, Werner J., and Vita Kumar. 2003. The impact of customer relationship characteristics on profitable lifetime duration. Journal of Marketing 67 (1): 77–99.

Rindfleisch, A., and C. Moorman. 2001. The acquisition and utilization of information in new product alliances: A strength-of-ties perspective. Journal of Marketing 65 (2): 1–18.

Risselada, Hans, Peter C. Verhoef, and Tammo H.A. Bijmolt. 2014. Dynamic effects of social influence and direct marketing on the adoption of high-technology products. Journal of Marketing 78 (2): 52–68.

Roberts, John H., Charles J. Nelson, and Pamela D. Morrison. 2005. a prelaunch diffusion model for evaluating market defense strategies. Marketing Science 24 (1): 150–164.

Robinson, W.T. 1988. Marketing mix reactions to entry. Marketing Science 7 (4): 368–385.

Robinson, W.T., and C. Fornell. 1985. Sources of market pioneer advantages in consumer goods industries. Journal of Marketing Research 22 (3): 305–317.

Rogers, Everett M. 2003. Diffusion of innovation , 5th edn. Free Press.

Rubera, Gaia. 2015. Design innovativeness and product sales’ evolution. Marketing Science 34 (1): 98–115.

Rubera, Gais, and Ahmet H. Kirca. 2012. Firm innovativeness and its performance outcomes: A meta-analytic review and theoretical integration. Journal of Marketing 76 (3): 130–147.

Sándor, Zsolt, and Michel Wedel. 2002. Profile construction in experimental choice designs for mixed logit models. Marketing Science 21 (4): 455–475.

Sándor, Zsolt, and Michel Wedel. 2005. Heterogeneous conjoint choice designs. Journal of Marketing Research 42 (2): 210–218.

Schmittlein, David C., Donald G. Morrison, and Richard Colombo. 1987. Counting your customers: Who are they and what will they do next? Management Science 33 (1): 1–24.

Sethi, R., and Z. Iqbal. 2008. Stage-gate controls, learning failure, and adverse effect on novel new products. Journal of Marketing 72 (1): 118–134.

Sethi, Rajesh, Zafar Iqbal, and Anju Sethi. 2012. Developing new-to-the-firm products: The role of micropolitical strategies. Journal of Marketing 76 (2): 99–115.

Sethi, R., D.C. Smith, and C.W. Park. 2001. Cross-functional product development teams, creativity, and the innovativeness of new consumer products. Journal of Marketing Research 38 (1): 73–85.

Shanker, V., G.S. Carpenter, and L. Krishnamurthi. 1998. Late mover advantage: How innovative late entrants outsell pioneers. Journal of Marketing Research 35 (February): 54–70.

Shocker, A.D., and V. Srinivasan. 1974. A consumer-based methodology for the identification of new product ideas. Management Science 20 (6): 921–937.

Silk, A.J., and G.L. Urban. 1978. Pre-test-market forecasting of new packaged goods: A model and measurement methodology. Journal of Marketing Research 15 (2): 171–191.

Slotegraaf, Rebecca, and Kwaku Atuahene-Gima. 2011. Product development team stability and new product advantage: The role of decision-making processes. Journal of Marketing 75 (1): 96–108.

Sonnier, Garrett P., Leigh McAlister, and Oliver J. Rutz. 2011. A dynamic model of the effect of online communications on firm sales. Marketing Science 30 (4): 702–716.

Sood, Ashish, Gareth M. James, Gerard J. Tellis, and Ji Zhu. 2012. Predicting the path of technological innovation: SAW vs. Moore, Bass, Gompertz, and Kryder. Marketing Science 31 (6): 964–979.

Sood, A., and G.J. Tellis. 2005. Technological evolution and radical innovation. Journal of Marketing 69 (3): 152–168.

Spann, Martin, Marc Fischer, and Gerard J. Tellis. 2015. Skimming or penetration? Strategic dynamic pricing for new products. Marketing Science 34 (2): 235–249.

Stephen, Andrew T., Peter Pal Zubcsek, and Jacob Goldenberg. 2015. Lower connectivity is better: The effects of network structure on redundancy of ideas and customer innovativeness in interdependent ideation tasks. Journal of Marketing (forthcoming).

Sun, Monic. 2012. How does the variance of product ratings matter? Management Science 58 (4): 696–707.

Tang, Tanya, Eric Fang, and Feng Wang. 2014. Is neutral really neutral? The effects of neutral user-generated content on product sales. Journal of Marketing 78 (4): 41–58.

Toubia, Olivier, Jacob Goldenberg, and Rosanna Garcia. 2014. improving penetration forecasts using social interactions data. Management Science 60 (12): 3049–3066.

Toubia, Olivier, John R. Hauser, and Duncan I. Simester. 2004. Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research 41 (1): 116–131.

Toubia, O., D.I. Simester, J.R. Hauser, and E. Dahan. 2003. Fast polyhedral adaptive conjoint estimation. Marketing Science 22 (3): 273–303.

Tracey, Paul, Jan B. Heide, and Simon J. Bell. 2014. Bringing ‘Place’ back in: Regional clusters, project governance, and new product outcomes. Journal of Marketing 78 (6): 1–16.

Urban, G.L. 1970. SPRINTER MOD III: A model for the analysis of new frequently purchased consumer products. Operations Research 18 (5): 805–854.

Urban, G. L. 1975. PERCEPTOR: A model for product positioning, Management Science , 21 (8): Application Series, 858–871.

Urban, G.L., T. Carter, S. Gaskin, and Z. Mucha. 1986. Market share rewards to pioneering brands: An empirical analysis and strategic implications. Management Science 32 (June): 645–659.

Urban, G.L., and J.R. Hauser. 2004. “Listening In” to find and explore new combinations of customer needs. Journal of Marketing 68 (2): 72–87.

Urban, G.L., J.R. Hauser, W.J. Qualls, B.D. Weinberg, J.D. Bohlmann, and R.A. Chicos. 1997. Validation and lessons from the field: Applications of information acceleration. Journal of Marketing Research 34 (February): 143–153.

Urban, G.L., and E. Von Hippel. 1988. Lead user analysis for the development of new industrial products. Management Science 34 (5): 569–582.

Urban, G.L., and G.M. Katz. 1983. Pre-test-market models: Validation and managerial implications. Journal of Marketing Research 20 (3): 221–234.

Urban, G.L., B.D. Weinberg, and J.R. Hauser. 1996. Premarket forecasting of really new products. Journal of Marketing 60 (1): 47–60.

Van den Bulte, Christophe, and Yogesh V. Joshi. 2007. New product diffusion with influentials and imitators. Marketing Science 26 (3): 400–421.

Von Hippel, E. 1986. Lead users: A source of novel product concepts. Management Science 32 (7): 791–805.

Wang, Qi, Yubo Chen, and Jinhong Xie. 2010. Survival in markets with network effects: Product Compatibility and order-of-entry effects. Journal of Marketing 74 (4): 1–14.

Wies, Simone, and Christine Moorman. 2015. Going public: How stock market listing changes firm innovation behavior. Journal of Marketing Research (forthcoming).

Wind, Y. 1973. A new procedure for concept evaluation. Journal of Marketing 37 (4): 2–11.

Wind, J., P.E. Green, D. Shifflet, and M. Scarbrough. 1989. Courtyard by Marriott: Designing a hotel facility with consumer-based marketing models. Interfaces 19 (1): 25–47.

Wittink, D.R., and P. Cattin. 1989. Commercial use of conjoint analysis: An update. Journal of Marketing 53 (3): 91–96.

Wittink, D.R., M. Vriens, and W. Burhenne. 1994. Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing 11: 41–52.

Download references

Author information

Authors and affiliations.

The Business School of Chinese University of Hong Kong, Sha Tin, Hong Kong

Tingting Fan

Tuck School of Business at Dartmouth, Hanover, NH, USA

Peter N. Golder

Columbia Business School, New York, NY, USA

Donald R. Lehmann

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Peter N. Golder .

Editor information

Editors and affiliations.

Rotterdam School of Management, Erasmus University, Rotterdam, South Holland, The Netherlands

Berend Wierenga

Department of Marketing, Hong Kong University of Science and Technology, Kowloon, Hong Kong

Ralf van der Lans

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Fan, T., Golder, P.N., Lehmann, D.R. (2017). Innovation and New Products Research: A State-of-the-Art Review, Models for Managerial Decision Making, and Future Research Directions. In: Wierenga, B., van der Lans, R. (eds) Handbook of Marketing Decision Models. International Series in Operations Research & Management Science, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-56941-3_3

Download citation

DOI : https://doi.org/10.1007/978-3-319-56941-3_3

Published : 14 July 2017

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-56939-0

Online ISBN : 978-3-319-56941-3

eBook Packages : Business and Management Business and Management (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Learn / Guides / Product research basics

Back to guides

A step-by-step guide to the product research process

A strong product research process ensures product teams maximize resources, meet key business goals, and make confident decisions that will deliver successful products and features to create customer delight.

But, how do you conduct effective product research?

Just as there’s no single way to develop a product, no single research process fits all product teams. But there are key steps that will help you balance business goals and user needs for actionable product research . 

This article takes you through the factors you should consider to tailor product research to your desired outcomes and provides a step-by-by-step guide to doing research right.

Use Hotjar to streamline your product research process

Hotjar offers product teams a rich stream of quantitative and qualitative data that keeps you connected to user needs at every stage of research.

What to consider before starting product research

Before jumping into the research process , product managers prepare their team. Take time to consider the why and determine how you can design the process to meet your unique product requirements. 

Reflect on:

Why you’re doing the research

Get connected with the deep purpose of your research: what you need to understand to create a profitable and effective product .

Determine specific outcomes of the research process.

During the early product discovery stages, generating new product ideas for innovation and getting to know your users better will serve as a solid foundation throughout the research process. At later stages, look for concrete feedback on a new product, or possible upgrades and feature updates for an existing product. The why behind the research should guide your process. 

Categorizing your users

Determining customer needs and segmenting users are crucial steps that impact the success of any product research strategy. 

You might use a random sample of potential or existing customers; or segment users according to region, industry, or other criteria to spot patterns across different demographics.

Trial users can give immediate product feedback, which is usually incredibly easy to implement (a new theme, for example) or incredibly difficult, like an entirely new functionality or platform for your product. Your long-time users can give nuanced feedback, but they overlook what doesn't work due to their expertise.

Finding that middle ground of users who like what you offer but aren't stuck to your brand is essential. These users appreciate being treated like their insights matter most—because they do.

Finding impartial user insights can be tricky since many tools track users who’ve been paid or incentivized to click through to your website or product. Product experience insights software like Hotjar can help by providing organic, unbiased user data that gives you a clear picture of your customer experience (CX) .

Pro tip: Hotjar Highlights lets you sort and curate user insights and attributes, and share them with your product team. You can also watch Session Recordings of users from specific countries or industries—or filter recordings to see only satisfied or dissatisfied user experiences, which can provide valuable information on what’s working (and what’s not).

A Hotjar Session Recording

Your core business goals 

The best product research processes overlap with the overall organizational vision, so update your research goals in line with company goals to ensure alignment. 

Designing your research process with cross-functional collaboration in mind is a great way to eliminate any communication issues, ensure all departments collect data that tests product profitability, business goals, and user satisfaction.

 Your team’s methodology

Different product methodologies emphasize different aspects of product research throughout its lifecycle, so it’s important to consider techniques that will fit your team’s working stages.

Teams who use waterfall methodologies usually rely on bursts of intense research before development and again during pre-launch. They also make a clear distinction between the product’s research and development phases. 

Teams who use agile, lean, or DevOps methods usually integrate research with the broader product development process, engaging in continuous discovery methods. 

Whatever your methodology, infuse research into every stage of the product lifecycle to achieve business goals like increased revenue, acquisitions, and user adoption.

Choosing which research tools to use

When you’re deciding how to do product research, you’ll need to consider your budget and company size to pick out your tool stack.

Manual research techniques like user interviews can be time-consuming and cost-intensive, but useful to forge a personal connection with users and ask improvised questions based on their responses.

Automated research tools (like Hotjar 👋) increase speed, efficiency, and cost-effectiveness, and reduce human error. They allow you to reach a larger target audience and ensure you’re getting clean, unbiased product feedback —in person, users are more likely to feel pressure to compliment your product or underplay their concerns, but with tools like Hotjar, you’ll get genuine, in-the-moment feedback from users as they engage with your product. 

Which team members will contribute

Involve different team members at each stage of the product workflow. For example, when you’re validating product ideas, you may want to include marketing and technical departments; and when you’re testing product usability , you may want to rely on the expertise of your engineers. 

It’s also important to consider what research other departments have done before launching your own process, so you don’t waste resources duplicating generic market research. 

8 steps for amazing product research

Amazing product research is all about doing smart research to unearth effective insights without getting lost in an information overload that derails your product workflow .  

Follow these eight steps to guide your product research strategies to achieve valuable, actionable product insights that will inform your product’s entire lifecycle, from ideation to execution. 

1. Define your research goals

First, set your high-level goals, which should test business objectives as well as customer-centric product discovery. These are often drawn directly from the product vision and strategy.

Then, create attainable, specific goals or questions for your team to focus on during each stage of their research. This might include: 

Conducting market research for the product’s adoption before its launch

Identifying areas where key features can be improved after the product launch

Evaluating the product’s performance throughout the product lifecycle

2. Understand your users

User needs are at the center of effective product research processes. 

Engage in user discovery—identify and understand your customer—as early as possible , even before you have definite product or feature ideas. Open-ended user research is a key source of product inspiration and innovation, and an essential step in determining product-market fit .

Then, when you have product proposals, prototypes, or a minimum viable product ( MVP) , you can start seeking more specific feedback. 

User research is all about interacting with your current or potential users and learning what they want and need . Developing a user-centric culture of ongoing research will help you gauge the market demand, position your product against the competition, and generate customer delight .

To create a user-centric research culture, conduct user interviews and create user personas. You can also connect more passively with your user demographic by looking at forums, Facebook groups, or sites like Reddit that are used by your customer niche. 

The more organic the research process, the better. It’s ideal to catch users in situations where they answer by instinct instead of having carefully crafted answers. It's what they say instinctively that leads to better product solutions.

Pro tip: use Feedback widgets to gather user feedback in a non-invasive way. 

Hotjar’s Feedback widgets are integrated into the product interface , so users can give quick feedback and then carry on with their tasks. This means you can survey your users and gain valuable insights by learning what they’re thinking and feeling as they interact with the product.

A Hotjar feedback widget

3. Do market research for your product 

Run thorough competitive and comparative analyses to test the business potential of your product against other solutions on the market , and engage in opportunity mapping to get stakeholder buy-in.

You can also use historical market data and trade reports to predict potential profitability and run keyword research to understand users and what potential customers are searching for to generate product ideas.

Once you’ve validated whether there’s a viable market for your product and determined how saturated that target market is, focus on your product’s unique selling points.

Pro tip: even if you already have a product established in a specific market, make sure to assess the market periodically. Markets and competitors change, and making assumptions because of your initial research processes can be a costly mistake. Work with your marketing team here to validate your ideas and avoid guesswork.

Evaluate your product regularly against the industry by creating a value curve. The value curve plots the product offerings currently available in the market on one axis, and the factors the industry is competing on and investing in heavily on the other. This can help you spot market opportunities, ensure product relevance, and get ideas for features you could add to increase user demand and open up new user bases.

Check out how Gavin increased conversions for his lead generation agency by 42% with Hotjar.

 4. Get to know industry trends

Next, combine your understanding of your users and market with research on technology trends that may affect user expectations of your product or its long-term viability. 

Stay on top of trends by regularly engaging with tech cultures —read trade magazines and news sites, listen to tech news podcasts, and follow key trendspotters on social media and specialist forums. You can also use tools like Google Trends , Trend Hunter , and PSFK . 

Another key source of tech trend information is your engineering team . Chances are, you have plenty of techies on your team who are up to speed on different aspects of technology and what’s forecasted to change.

Pro tip: rigorously analyze trends and put them into context to understand what has staying power, as you avoid jumping on every passing fad. Create a learning culture that embraces experimentation and gives team members the opportunity to share their knowledge. 

Analyze the latest trending topics and projects in mainstream open-source communities across the Internet such as GitHub. These communities are an incredible resource for identifying tech trends that are sustainable, disruptive, and have immense staying power. 

It's also important to subscribe to prominent tech publications and leading technology platforms such as Azure and AWS to get the latest tech news and new feature announcements delivered directly to your inbox. This way, your product team is always in the know about the most important tech trends that are shaping product development and product markets.

5. Validate ideas with current or potential users

Once you’ve developed a strong sense of your users, market, and technology, it’s time to start testing concrete ideas and solutions. 

Based on your early research, identify possible products, features, or upgrades that could meet user needs as well as business goals. Then, run concept testing to evaluate the user experience.

First, identify key users or user types to test. Recruit participants for customer interviews or focus groups, or deploy Hotjar Surveys , Incoming Feedback tools, and Session Recordings to test ideas with existing users. 

Then, ask questions or set tasks and observe user responses. You may just want to explain concepts to users at this stage—or you can use wireframes or mockups; or, at later stages, prototypes or MVPs. 

Make sure you account for confirmation bias and false-positive responses from users when designing the validation process. Include open- and closed-ended questions and use measures like purchase intent to determine customer adoption.

Pro tip: use fake door testing to gauge interest in new features across your existing user base. 

In fake door tests, you show users a call-to-action for a product action that doesn’t exist yet. Once they click to perform the action, they’ll be taken to a page that explains this feature isn’t available yet—you may also choose to include a short survey on this page to learn more about their interest. By reviewing answers to survey questions and the click-through rate , product teams can quickly validate ideas for new features or improvements with users.

6. Test your MVP

The next step in your product research process is to develop a Minimum Viable Product based on validated ideas and run tests to improve subsequent iterations. 

This is a critical stage in product research that you shouldn’t skip. Waiting for the fully developed product before running tests makes it harder to fix software and prioritize bug issues, causing major delays. 

Quality assurance (QA) testing, regression testing, and performance testing check the MVP’s functionality and show developers where they need to make product changes . 

User tests are also key at this stage. Different types of product testing , like tree testing and card sorting, can confirm whether users can easily navigate your product to find the functionality they need. 

A/B tests and multivariate tests , where you split your user base into groups and give them different versions of a product or feature, can help you decide which iteration to run with. Hotjar Heatmaps allow you to easily compare where users click and scroll on different versions of the product.

7. Continue research after the product launch

Consider doing a soft launch—or even canary deployment—where you release new products or features to a small group of users

Gather data to weed out bugs

Finally, adapt the product based on user responses

Then you can roll it out to all users.

But even once you’ve launched the final product, your research isn’t over. The best product teams stay connected with their users and regularly analyze market trends and tech changes.

After the product is released, either through a soft launch or a regular launch, implementing a data-driven approach to the go-to-market strategy is crucial in parsing consumer reports and validating trends and customer opinions.

Continuous research ensures that your product stays relevant and successfully meets customer needs, which will boost user metrics and business metrics alike.

So how can you continue your research throughout the product lifecycle? 

Watch session recordings to spot blockers and bugs where users are rage clicking or dropping off the product journey

Use heatmaps to understand which product elements are most popular—and unpopular—with users

Measure product analytics like click-through rate (CTR) and product conversion rate

Stay up to date on industry and market trends 

Incorporate regular opportunities for cross-team discussions to get different research perspectives

Schedule regular user and customer interviews

Use product experience insights tools like Hotjar to give you a steady stream of user feedback through Surveys and Feedback widgets

8. Turn research into action

The final step in any product research process is to organize your research and turn insights into action. 

Curate your research into specific, actionable themes to cut through the noise and gather valuable, user-centric insights.

Then, use your research to establish a strong product strategy and roadmap to guide your product development process. Make sure you compare the strategy and roadmap with new research at regular intervals and update where needed, though it’s important to strike a balance: these documents should be dynamic but relatively stable touchpoints.  

Your product research should also drive your day-to-day decisions and product backlog management , and form the basis of your product storytelling to help get stakeholder buy-in. 

Why creating a user-centric research culture is essential

Remember: at heart, all product research is user research. 

Product teams who are endlessly curious about their users—who they are, what they need, how they experience your product—can better meet the demands of an ever-evolving market, inspire customer loyalty, and increase their Net Promoter Score (NPS) . With a learning mindset and a commitment to customer-centric product discovery, you can transform research into innovation and sustainable business growth .

FAQs on the product research process

What is product research.

Product research is the process of gathering data about your product’s purpose, intended users, and market to meet user needs and achieve business goals.

What are the steps in the product research process?

The 8 steps in an effective product research process are: 

1) Define your research goals

2) Understand your users

3) Do market research for your product

4) Get to know industry trends

5) Validate ideas with current or potential users

6) Test your MVP

7) Continue research after the product launch

8) Turn research into action

Why is product research important?

Strong product research is critical to product management because: 

It ensures the product will meet customer needs and hit business targets 

It helps product managers (PMs) develop a data-informed product vision, strategy, and roadmap

It helps PMs make confident decisions on the product backlog and day-to-day tasks

It keeps the product team motivated and connected with the purpose of their work 

It helps the product team communicate product value to stakeholders to get buy-in and secure resources

Prioritize product features

Previous chapter

Guide index

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier Sponsored Documents

The role of product development practices on new product performance: Evidence from Nigeria's financial services providers

Nkemdilim iheanachor.

a Strategy and International Business Group of Lagos Business School, Pan-Atlantic University, Lagos, Nigeria

Immanuel Ovemeso Umukoro

b Information Systems & Digital Business Transformation, Lagos Business School, Pan-Atlantic University, Lagos, Nigeria

Olayinka David-West

c Professor of Information Systems, Lagos Business School, Pan-Atlantic University, Lagos, Nigeria

Associated Data

• • Impact of product development practices on the performance of newly launched products.
• • When key product development practices are not well implemented the likelihood of product failure increases.
• • Development of financial service products affect adoption, use and product penetration.
• • Management teams of various financial service providers should invest in developing sound product development practices.

This study investigates the impact of product development practices on the performance of new financial products and services through the analysis of ten in-depth case studies. We argue that weak product development practices negatively affect product performance. This study finds that in Nigeria, new financial product performance is suboptimal because of poor product development practices. This study further shows that when poor execution follows inadequate product development practices, the likelihood of product failure increases, as evidenced by poor product performance and low adoption. The processes adopted in the development of financial services affect the adoption, use, and overall penetration of the product in the target market. Therefore, this study suggests that the management team of various financial service providers invest in developing sound product development practices in the actualization of their goals of increasing the adoption and use of their products.

1. Introduction

In emerging markets, financial services innovation (whether in products or business models) is critical to a provider's sustainability and competitiveness ( David-West et al., (2019) ). Technological innovation has disrupted the financial services sector by establishing novel ways of creating and delivering value to customers ( Hine and Greenaway, 2016 ; Li, 2016; Madeira, 2016 ). This warrants additional investments in information technology (IT) assets, resources and capabilities. As IT investments increase, customer needs are also becoming more diverse. Financial service providers (FSPs) must also strengthen their processes to meet and surpass the needs of their customers who are the most influential yet volatile stakeholders ( De Oliveira and Rabechini, 2019 ; Kim et al., 2018 ; Ouma et al., 2017 ; Pollari, 2016 ).

Understanding consumer pain points remains a critical driver in creating and delivering a compelling customer value proposition that meets their diverse financial service needs. David-West et al., 2019 report that customer value propositions (CVPs) such as affordability, accessibility, ease of use, service reliability and security characterize financial products and services. FSPs need these CVPs to acquire and retain customers. To achieve and sustain these attributes in their products, FSPs must continually review their customer value propositions and product fit amidst changing consumer behaviours. The continuous review of product development practices is one such approach. This is undoubtedly true, especially in an era where new non-bank financial technology (Fintech) firms are challenging the incumbents and disrupting the financial services space with new value propositions.

Financial product development requires the commitment of critical resources and an understanding of customer needs, characteristics and behaviours to gain the adoption, customer satisfaction ( Brockmann, 2017 ; Laukkanen, 2016 ; Park and Koh, 2017 ), and continuous use of financial services ( David-West et al., 2019b ). Well-developed financial products yield benefits such as improved market shares, higher profits, returns on equity, customer loyalty, and long-term survival ( Albers et al., 2018 ; Lalic et al., 2018 ; Mikkola, 2018 ). For new products to be successful, organizations must ensure that product development processes such as ideation, prototyping, testing and launching must be carefully and systematically executed ( Roy et al., 2017 ; Yoon and Rim, 2018 ; Claudy et al., 2016 ). Beyond the value proposition, pilot and launch strategies that FSPs adopt can also affect product acceptance. There is, therefore, a need to be innovative in the product development process. Such processes require a competent team, efficient operational processes, and a strategy for managing emerging product risks.

The spike in financial product failure in emerging economies is caused by a lack of adherence to good product development practices ( Albers et al., 2018 ; Lalic et., 2018; Mikkola, 2018 ). For instance, low financial inclusion rates in Nigeria can be attributed to poor product adoption resulting from poor product-customer fit and other exclusion enablers. Evidence of failed financial products abounds in Asia ( Kim et al., 2016 ) and Africa ( Karabag, 2019 ; Šoltés et al., 2017 ; Gumel, 2017 ). These products often fail to address customers’ pain points. Likewise, product failures have significant impacts on customers, employees, profitability, market share, brand equity, investors, and the economy at large. Such failures can erode consumers’ trust in a brand and could be capitalized upon by rival firms. This study investigates the impact of product development practices on the performance of newly launched products by financial service providers by relying on evidence provided by ten case studies of purposively sampled FSPs.

The goal of product development practices is to meet consumer needs while keeping a focus on profitability and business sustainability. Products are one dimension of competition in the financial services space. The practices that produce the products must receive adequate attention. They are vital to improving the quality of services to promote customer adoption, consumer satisfaction, customer retention, profitability and long-term sustainability.

1.1. Rationale

The concept of product development has been widely discussed in the literature, especially in innovation management research. However, the analysis of the literature on product development practices shows that most references and case studies are in the manufacturing ( Akroush and Awwad, 2018 ; Chang and Taylor, 2016 ; Vinayak and Kodali, 2014 ), telecom ( Namusonge et al., 2017 ), aviation ( Naghi Ganji et al., 2017 ) and transportation sectors. Studies on product development practices of financial service providers remain sparse, especially those from an emerging economy perspective. This phenomenon is essential given the low level of adoption of financial services in Nigeria, resulting in a low financial inclusion rate despite the broad spectrum of financial products and services offered by Nigerian FSPs. Where financial products exist, their adoption and utility are usually low. The adoption of financial services (used interchangeably with financial products) is a significant phenomenon as it plays a critical role in enabling Nigeria to attain its financial inclusion goals of 80% by 2020. Despite the high number of bank and non-bank FSPs in Nigeria, approximately 36.8% (36.6 million) of the adult population remains financially excluded. Additionally, 14.6% (14.6 million) of adults are underserved. The underserved segment resorts to the use of informal channels and services ( EFInA, 2018 ), which could be costly and risky.

The challenge of financial inclusion in Nigeria and its consequent negative impact on economic development continues to attract the attention of scholars and governments at all levels. Several factors are responsible for this low level of adoption. While a demand-side approach may amplify the voice of financial services consumers, a supply-side approach provides a broader perspective on the processes through which FSPs conceptualize and develop products. This can offer insight into how product development practices may contribute to low financial service adoption and, by extension, low financial inclusion. Financial exclusion can be due to lack of product-customer fit, often resulting from flawed research and development efforts. This can be because of insufficient business case analysis, ineffective market segmentation, the absence of product prototype development, and insufficient testing and refinement, among other causes.

Specifically, the study examines the following:

• 1 The nature of product development practices (PDPs) among financial services providers (FSPs) in Nigeria
• 2 The impact of product development processes on new product performance in Nigeria
• 3 How the risk management strategies of financial services providers affect the performance of the new products of financial service providers

1.2. Research questions

The following research questions guided this study:

• 1 How do Nigerian FSPs undertake product development?
• 2 To what extent do best product development processes guide Nigerian FSPs in their new product development efforts?
• 3 What is the role of product development processes in the performance of new products developed by Nigerian FSPs?

We organize the rest of this paper as follows. The section after this introduction provides a brief review of the related literature on product development practices. It aims to explore the relevant literature and outline extant theories within the context of product development practices. The method section follows the literature review. The results and discussion sections are next; followed by a conclusion, recommendations, implications for practice and directions for further studies.

2. Literature review

2.1. product development and product development practices.

The term product development or new product development refers to the transformation process of a market opportunity and a set of assumptions regarding product technology into a product accessible to the market ( Chang and Taylor, 2016 ). It is a process that leads to introducing new products into a market as a response to a market opportunity by logically combining a set of activities. Product development practices (PDPs) are a defined set of tasks, steps and phases that describe the standards by which a company repetitively converts embryonic ideas into sellable products or services (Kahn, 2004). They are firm practices that translate into the development and launch of new products as a response to new market opportunities. PDPs are "success drivers of new product development efforts" ( Troy et al., 2008 , p. 136) because when properly implemented, they can positively impact an organization's market share, profitability and long-term survival. PDPs include practices that help business organizations arrive at quality and viable products that meet market needs and can capture value for the organization while creating value for customers (see Fig. 1 ). The concept impacts three broad aspects of organizational success: operational, financial, and marketing performance.

Product development framework ( Source: Author's representation ).

Nguyen et al. (2018) note that practices such as the development of product programmes, research and development (R&D) and innovation can translate into the success of a new product. There are two categories of PDPs – process speed and integrative practices.

• ■ Process Speed: This refers to the compression of activities ( Kiss and Barr, 2017 ) versus traditional sequential new product development practices. Process speed could be agile development, early feedback or late decision-making product development practices for accelerating the speed of the product development process. Agile development is characterized by rapid development iterations used to gain feedback combined with overlapping processes where the next iteration begins before the current iteration finishes ( Haidar et al., 2017 ; Mohan et al., 2010 ). Agile development contrasts with the traditional waterfall development methodology that focuses on preparing a complete and detailed design specification before the execution phase begins ( Guntamukkala et al., 2006 ; Roems, 2017 ). Early feedback  refers to regularly gathering feedback from multiple constituents at the earliest stages of the product development process ( Lakemond et al., 2013 ; Thomas, 2014 ; Narasimhan et al., 2006 ). Late decision making is a process in which product concepts, capabilities and designs are not finalized until the last phases of the development process ( Buganza et al., 2010 , 2009 ). Late decision-making contrasts with the traditional stage-gate style processes where product development is in a sequential structure of decision gates (Kahn, 2004). At each decision gate, a facet of the product is agreed upon and frozen before moving to the next gate.
• ■ Integrative P ractices: These are processes used by the organization to regenerate its knowledge base (Eisenhardt and Martin, 2000; Kogut and Zander, 1992 ; Marsh and Stock, 2006 ). They include foundational customers and supplier participation. Foundational customers are customer representatives who participate in the new product development process in a manner that helps shape the requirement analysis ( Carbonell et al., 2009 ; Gatignon and Xuereb, 1997 ) for new product development. In-depth requirement analysis of market realities is critical for the success of financial products. Validating initial market assumptions requires engaging customers that can provide a near real-life input to the requirement analysis and initial product design stages. Supplier participation refers to the various roles that suppliers play in the product development process. It ranges from merely delivering parts based on a specification to substantial involvement in the design process ( Ragatz et al., 2002 ; Gatignon and Xuereb, 1997 ; Cusumano and Takeishi, 1991 ). Suppliers are a critical category of stakeholders in the product development process. The interface with customers provides useful feedback on customer buying and consumption behavior. Lau, 2011 also state that supplier involvement and inter-functional integration can also eliminate barriers that lead to new product failure.

2.2. Product performance and performance measures

Product performance refers to how well a product performs across defined measurement indicators. The indicators could be how product development promotes customer attraction (market share) and retention, revenues and net profit, brand equity, customer satisfaction and feedback, among other indicators ( Namusonge et al., 2017 ; Ganeshkumar and Nambirajan, 2013 ; Schilling and Hill, 2005 ). Product performance reflects the financial and market performance of a firm's new or existing product ( Najafi-Tavani et al. (2016) ). Product performance measures or outcomes are the actual performance of a product against the expected level of performance. They are indicators that measure changes that the firm needs to manage the transition towards defined goals. In examining how firms benefit from new product development, we can broadly categorize product performance into four distinct dimensions:

• ■ Profitability (financial) Performance : Financial performance is the degree to which the product exceeds or falls short of the expected profitability level ( Cooper, 2019 ). The profitability dimension includes both the level of profit and profit objectives. On average, the level of profit is scored relatively higher than the score for profit against the objective. It, therefore, implies that firms expect a higher level of profit from introducing new products.
• ■ Sales and Market Performance : Market performance is the extent to which the product exceeds or falls short of achieving market expectations ( Cooper, 2019 ). Sales performance illustrates the performance of new products and comprises measures of performance relative to sales objectives, and measures of total sales. Sales performance is growth in sales against the aim.
• ■ Customer Satisfaction :  This is the level of the purchaser's affective response. It is an assessment indicator of how well financial services perform. High adoption is due to customer satisfaction with the product. However, Umukoro and Tiamiyu (2017) warn that in the context of e-services, the absence of better alternatives may increase use without necessarily translating into customer satisfaction. One way of ensuring customer satisfaction is for FSPs to define a product value proposition in ways that address customer needs ( David-West et al., 2019b , 2017 ; Mbiti and Weil, 2013 ).
• ■ Enhanced Opportunities : These are the gains of product development practices to the firm as an entity rather than solely accruing to the product. This factor illustrates the long-term benefits that can be derived from introducing a new product. Repositioning the firm, creating a new market, and platforms for the introduction of additional new products or new product features increase the potential for long-term prosperity.

2.3. Product development practices and product performance (success)

Schilling and Hill 1998 suggest that for a firm to be successful at new product development, it must simultaneously meet two critical objectives: maximizing profits through customer needs and minimizing the time to market. While these objectives often pose conflicting demands on a firm, there is a growing body of evidence that a firm may adopt strategies to meet these objectives successfully. Successful companies are known for articulating their strategic plan and leveraging their R&D portfolio to achieve a fit between their new product development goals and their current resources and competencies. Namusonge et al (2017) posits that strategic product development practices have a positive and significant influence on financial performance.

Many products fail too quickly because of weak market analysis, poor design (weak products), regulatory risks, weak and unvalidated market assumptions, and late arrival to market, among other reasons. Ateke et al. (2015) note that firms can also measure the performance of a new product in terms of the levels of customer adoption and satisfaction, the profitability of the new product, and how long the product survives competition from rival products. It is important to note that how well a new product performs in terms of financial performance, customer adoption, growth in market share, and customer satisfaction is a function of the product development practices that the firm adopts ( Nguyen et al., 2018 ).

Successful products need a strong product development team to conduct practices that foster the success of developed products ( Naghi Ganji et al., 2017 ). Product managers must, therefore, understand the business impacts of product development decisions and the need to have the right product development practices in place ( Nguyen et al., 2018 ). Often, product development managers are quick to isolate the causes of poor product performance and may tackle them individually. However, a combination of these factors may exist. Cassia et al. (2012) argue that new products and ideas fail because they lack structured product development processes or practices. The absence of efficient product development practices such as risk management, product development strategy, research and development and other practices can lead to weak products (Almeida and Miguel, 2007). This can lead to poor market and requirement analysis resulting to products that do not align with customer needs.

2.4. Product development practices include the following

• ■ Research and Development: Product innovation begins with an understanding of a need and how well to solve the need. Frankort (2016) reports that knowledge acquisition through R&D is positively associated with product performance both in terms of product breadth and market performance. Many organizations, including those in the financial service sector, are engaging in R&D to be more deliberate in the products they introduce. Organizations such as Google, Apple, and Microsoft take R&D further and include the establishment of research, development and innovation labs to aid their product development efforts. With greater involvement in R&D, products perform better in terms of profitability, adoption, and usage ( Cuervo-Cazurra et al., 2018 ; Santoro et al., 2017 ; Homburg et al., 2017 ; ). Considering this, we argue the following:

Proposition 1: Research and development practices enhance new financial product performance.

• ■ Well-established or Structured Product Development Processes: Product development involves a logical implementation of a set of activities ( Chang and Taylor, 2016 ; Kahn, 2004). Good product development practices include well-thought-out processes that follow a product development methodology. Although many methodologies exist, certain features characterize them. For instance, product requirement analysis is a necessary process or practice that must be undertaken to understand customer and market dynamics and validate initial market assumptions. A well-structured product development process also considers critical activities in the product development life cycle. These activities include customer empathy and ideation, the determination of a business case, design and prototyping, testing and launch, and product performance assessment. Given these assumptions, we argue the following:

Proposition 2: Structured product development processes contribute to the success of new financial products.

• ■ Product Development Strategy: The product strategy is a plan that focuses on the product efforts directed towards achieving business goals. It is a set of actions in a sequence explaining why this is the right approach. Poor products can also result from a poor product strategy, given that the strategy determines the products’ impact and performance. Product development efforts become aimless without a defined strategy, just as a strategy is useless without execution. The product development strategy helps contextualize the problem that the product will solve, for whom, when and where should a new financial product be introduced. A well-articulated product strategy helps a firm to assess how its product development capabilities match the market opportunities. Such capabilities may include leadership, functional and technical skills. Where existing capabilities are inadequate for exploring market opportunities, the firm must strategize on how to play, where to play, and when to play to win (Ogechie, 2018). This can significantly increase the chances that the proposed product will perform well when finally developed. Consequently, we argue the following:

Proposition 3: The existence of a product development strategy enhances the success (performance) of new financial products.

• ■ Risk Management: New product development efforts often face risks that, when not well managed, may lead to product failure; and sometimes, product development efforts may not even materialize. Risks differ across different organizations and product lines. While the risk profiles for different financial products may not be identical, it is essential to identify where on the spectrum a company wants to be to plan risk mitigation measures. Consequently, we argue the following:

Proposition 4: Risk management practices can enhance the performance of new financial products.

2.5. Theoretical foundation and research framework

The dynamic capabilities (DCs) theory extends the well-established resource-based view (RBV) theory. The dynamic capabilities theory emphasizes the ability of a firm to integrate, develop and reinvigorate its internal capacity to address challenges arising from rapidly changing business environments ( Teece et al., 1997 , p. 516). From the above definition, we can infer that DCs promote continuous change and the configuration of the productive resources of a firm to adapt better to the environment.

The literature provides empirical evidence that suggests that the management of various competitive organizations invests in product development practices as a strategic solution for long-term survival in some dynamic environments (e.g., Pavlou and El Sawy, 2011 ; Schilke, 2014 ). Regular product development practices (PDPs) and product introduction require a variety of activities that are the driving forces to regenerate and renew the routines and competitors' strategies of a firm, ensuring environmental adaptation in various industries ( Helfat and Winter, 2011 ). The DC theory provides the underpinning for this case study on the product development practices of financial services as it helps to explain how financial service providers develop and integrate assets, resources and capabilities for new product development as a response to the needs of a changing business environment.

Fig. 2 shows four critical product development practices – product development processes, risk management, research and development, and product strategy – as factors that affect the performance of new financial products.

Research framework.

3. Methodology

This paper is an exploratory study. It adopts a qualitative method using multiple case studies of eight (8) financial services providers to investigate the product development practices and the effects of these practices on new product performance. Case studies provide very engaging and rich explorations of a project as it develops in a real-world setting ( Berkowitz, 1997 ). The study uses a cross-case analysis given its robustness for analysis and synthesis of data across multiple sources, unlike the individual or intra-case analysis approach that restricts the analysis to a single case ( Cruzes et al., 2015 ; Miles et al., 2013 ; Berkowitz, 1997 ; Mahoney, 1997 ;). We collected data on product development practices using semi-structured interviews.

Theories and concepts from the existing literature were identified (see Table 1 ) and used in the development of question items for the interview guide. Pertinent questions were framed and validated for each of the constructs. We derived the questions from an item generation process while incorporating themes, noting patterns, seeing plausibility, clustering, making metaphors, counting, contrasting/comparing and partitioning variables ( David-West et al., 2018 ; Miles et al., 2013 ). The final instrument is a semi-structured interview developed from the validation of questions conducted through several iterations of expert review. Convenient and purposive sampling of FSPs (cases) was conducted to select the respondents who were within reach. The purposiveness of the sampling approach ensures that the data were collected from product development managers or senior team members (see Table 1 ) at FSP headquarters, where product development and decision-making processes are located.

Profile of interviewees.

*All respondents interviewed are of the managerial cadre.

Table 1 highlights the sampling profiles used in the study. Embedded research ethics protocols guided the practices used in seeking formal participation consent and session recordings. An a priori list of codes guided the coding and analysis of interview transcripts. The hierarchical code structure from the a priori list of codes was replicated in an Nvivo QDA environment ( David-West et al., 2018 ).

4.1. Demographic characteristics of the interviewed FSPs

In Table 2 , we present the main characteristics of these ten FSPs. For the reason of confidentiality, financial services providers have been anonymized in the table.

Main features of selected FSPs for multiple case studies.

Many of the FSPs interviewed mentioned that several financial services were developed within the last five years. As noted earlier, a plethora of financial products has always characterized the Nigerian financial service market. However, many of these products underperform. The products shown in Table 2 can be broadly grouped into savings, credits, insurance, pensions, utility and bill payment, corporate banking, SME banking, and remittance products.

4.2. Product development practices and level of implementation by FSPs

Table 3 summarises the different product development practices of FSPs and the FSPs' performance levels on each of those practices reported. The dominant product development practices of FSPs include R&D, product design and prototyping, risk management, product performance measurement, strategy formulation and execution, and impact measurement. As shown in Table 3 , all FSPs reported engaging in all the product development practices that were identified. In attempting to assess how product development practices affect new product performance, the study also assessed the level of implementation of these product development practices. The results are presented in Table 3 .

Product Development Practices (PDPs) and FSPs Scorecard on each PDP.

The results presented in Table 3 show that the level of execution of product development practices varies across different FSPs. However, similar patterns exist among FSPs with similar assets, resources, and capabilities (ARCs). The results are discussed in the following section.

5. Discussion

5.1. nature of product development practices among nigerian fsps.

We assess the product development practices conducted by the different financial service providers to understand the differences and similarities and the attendant's reasons for the level of PDPs conducted.

• • Product Development Practices among Mobile Money Operators (MMOs): Mobile money operators (FSPs 2, 4, 8, & 9) performed low or moderate across the PDP measures. These institutions primarily provide payment services through mobile devices. They rely mainly on the quality of mobile network connectivity and the spread of their agents across different locations of interest. MMOs typically offer higher-priced services because of the high service charges they incur from different partners who provide the service delivery channels. Manpower costs are high, which limits the product development capabilities of these providers. The assessed risk is average as payment services do not involve the extension of credit. MMOs have a significant need for high-quality talent, which is expensive and scarce. Market sizing and product viability assessments are non-existent. Market and occupational segmentation are also not visible, although product development and deployment are driven by profitability assessments. Low levels of ideation and market testing are observed.
• • Product Development Practices among Pension and Insurance Providers: Pension and insurance providers scored low in R&D, customer empathy and ideation, business case determination, product design, prototyping, testing and launch, and product impact assessment, although both FSPs (7 and 10) scored moderately in their risk assessment practices. These institutions primarily focus on providing affordable retirement savings to working individuals to make them financially secure and independent in their old age. Most product offerings are homogenous, and the product design exists only within the regulatory boundaries defined by PENCOM.

These regulatory safeguards are heavily skewed towards risk management with little incentives to potential savers compared with other FSPs that offer them credit. They mostly engage in product adaptation rather than developing new products to meet the needs of banked and served consumers. Risk management here is very strong while leveraging the risk management capabilities of the parent pension companies. Market sizing and product viability assessments are very scant. They do not have products that are gender specific, but they have products that speak to the general needs of the consumers outside the pension net. We also observed a low level of prototyping and product testing among pension and insurance providers.

• • Product Development Practices among Microfinance Banks/Institutions: These institutions (FSPs 1 and 5) also exhibited similar product development practices. While they score moderate across several PDPs, more attention is given to business case analysis, product design, and prototyping. These institutions are part of the FSP segment in Nigeria that customizes services with ethical lending practices in the form of small business loans to unemployed, low-income individuals. These people would otherwise have limited or no access to other financial products, especially in semi-urban and rural areas. There are no procedures for how to track product performance. The most popular products are near-identical. It takes from two months to two years for them to launch new products. The prototyping and actual product development process are also unclear. MFBs do not have products that are gender specific, and they have fewer cost reduction initiatives in terms of interest rates. MFBs identify the need for new products through agent networks, organic movement, and interviews (FGDs) that provide them with an understanding of the dynamics of consumer behaviours and locations.
• • Product Development Practices among Deposit Money (Commercial) Banks: Although deposit money banks (FSPs 3 & 6) are not very similar in product development practices, none of the DMBs scored high in any of the product development practices. DMBs have well-resourced product development teams that come from diverse functional areas in the organization. Providers in this area conduct more product adaptation and offer the same product to virtually all consumers. Minimal customization occurs. These factors lead to low product adoption; and where adoption exists, customer attrition is high. Risk assessment is high, and this could reduce adoption. Market sizing and product viability assessments are very scant. Market and occupational segmentation are shallow as providers rely more on customer feedback to gage market needs, some of which are unstructured or inaccurate. For DMBs, product development is better resourced. Product testing and roll-out can take an average of 2 to 3 months with regulatory approval not serving as a major impediment. A low level of ideation is widespread and could be the reason for the product homogeneity that is observed among DMBs.

5.2. Product development practices and product performance

Proposition 1: Research and Development Practices and Product Performance

In the first proposition, we argue that R&D enhances new financial service performance. All FSPs studied scored low or average on R&D efforts. The findings of this study suggest that poor R&D practice is one reason financial services fail in Nigeria. For instance, a respondent stated the following:

"…our research and development are mainly on market research to map out where the unbanked and under-banked customers are located. That way, our products can be more targeted in our marketing efforts."

This is not an R&D effort that defines the product development direction, but rather it is research that helps the marketing of the product. Beyond demographic profiling, R&D entails behavioural and psychometric profiling of the target market segments to understand their economic lives and the reasons for their behaviours. R&D efforts that consider human-centred design approaches can unravel the various consumer archetypes across various demographic profiles. It is in this stage that the team empathizes with the customer to ideate and refine the value proposition. A good R&D effort provides a clear picture of the problem and potential solutions. A respondent notes thus:

"…I have been involved in some ideation where we have to engage women in some market places to ask them what are the pain points, what are the things that we need to know. We are trying to build a product that will help them to save more and trying to attach some new product to it but we found that what we were thinking about was not what they even wanted and that led to the introduction of a new product to them."

Grimpe et al. (2017) note that innovative product design, packaging, pricing, and promotion that are rooted in R&D can significantly drive the performance of new products. Processes driven by R&D can guide the product development team during the design, prototyping, and testing phases. We conclude that the low level of research and development among FSPs is a driver of the poor performance of new financial services (products) in the Nigerian market.

Another respondent states the following:

"The number of unbanked and under-banked adults in Nigeria is about 50 percent of the adult population. We aim to reach those in places that no one is interested in."

Products developed with this mindset will fail. While the market may exist, not all of it is addressable. Serving a dynamic market such as Nigeria requires quality R&D effort. D'Este et al. (2016) stress that there is clear evidence that firms' knowledge creation capacities, especially internal R&D activities, are decisive for their product performance.

Proposition 2: Product Development Processes and Product Performance

We argue for structured product development processes. They include customer empathy, ideation, concept testing and refinement, requirement analysis and market validation, prototyping, piloting, product lunch and performance assessment. They contribute to the success of new financial products. The data show that FSPs scored low on each product development process except on indicators such as business case determination, design and prototyping, and ideation where a few FSPs scored high (see Table 3 ). Many FSPs deploy homogenous products to consumers without following a rigorous product development process guided by an established product development methodology. Some FSPs do not conduct customization that ensures that the products meet the needs of the diverse mass market they aim to serve. Homogenous products do not address diverse customer needs and may lead to poor product performance. One of the senior executives interviewed remarked the following:

"…we trust our product development team because they come with a wealth of experience having worked in the manufacturing industry for years. They understand how to carry out product development, and we give them full support once they can show the profitability of the product."

The products that meet the needs of this market may vary across demographic groups and would require understanding customer needs, defining product requirements and business cases, and scenario planning. Good product development practices require product teams to be methodological ( Kauffman et al., 2015 ; Orbach and Fruchter, 2011 ). These methodological approaches can help in developing products in a manner that considers different customer archetypes across different demographic profiles. This helps to achieve well-defined use cases and customer-centric products with guaranteed wide adoption and profitability. Well-defined and executed product development processes can also help reduce product failures ( D'Este et al., 2016 ) as product development teams can run more iterations of prototypes, allowing for quality checks and determining the product's desirability, viability and feasibility. We conclude that the absence of or a poor implementation of these processes can translate into product failure.

Proposition 3: Product Development Strategy and Product Development Performance

Here, we argue that the availability of a product development strategy enhances the success (performance) of new financial products, given that strategy is required to serve a market efficiently and profitably. Grimpe et al. (2017) posit that a well-developed strategy is critical to successfully taking a product to the market as it considers market forces, especially the threat of substitute products, barriers to entry and other forces that may lower product performance. Product strategies are unique to specific products. The route-to-market (RTM) also differs across locations and demographics. The findings also show that all the FSPs scored low on the product strategy indicator. What many FSPs treat as a product strategy are marketing plans detailing how they will undertake branding and advertising. A respondent notes as follows:

"…sometimes we have partnerships to deploy some products, so we have to work with banks, like I said pension companies, sometimes we . . …there is a small scale pilot which is with the internal customers, which is me and my colleagues so we are the internal customers, and then expanded retail team, who are the guys who manage the retail product on the field and then we also sample a number of our key agent for a soft life deployment before giving out the products to the customers in general…"

Product pricing is the responsibility of the finance department, just as operations are an HR concern. There was no strategy document showing how the product will translate into the realization of the overall organization vision. The absence of this has resulted in poor product performance measurement. Drawing from Danneels's (2002) first- and second-order competencies, we can explain that a product strategy helps product managers and teams build marketing innovations that help in aligning new product performance metrics to the overall organizational goal. The absence of this can negatively affect product performance as there are likely to be undefined indicators or approaches for measuring product performance or for taking appropriate corrective measures.

Proposition 4: Risk Management Practices and Product Performance

Proposition four argues that risk management practices can enhance the performance of new financial products. The study observes that the risk management practices in product management are suboptimal among Nigerian FSPs. Few FSPs (mainly MMOs), however, prioritized risk. An MMO representative stated as follows:

"Like I said at the product conceptualization and development, all the functionary units including risk are involved. So, all the risk exposure deliberations are handled at that stage. We have what we call the product papers…in that document every aspect of that product is articulated and documented including the risk mitigants."

A key area where risk is dominant is the platform and not risks relating to product development. FSPs are more concerned with managing risks that are associated with the security of their platforms while neglecting risks that may arise from non-approval by regulators. Such risks, when poorly managed, may lead to products not making it to the hands of the target user groups because of non-approval from regulatory bodies.

6. Conclusion

New financial products in Nigeria struggle to perform well because of poor product development practices. Although the financial service sector has grown over the years with an improved regulatory environment, this study shows that product development practices seeking to guarantee new product success are poorly implemented. The resultant effects are poor product performance and low adoption. The processes adopted in the development of financial services affect the adoption, use, and overall penetration of the product in the marketplace. Financial inclusion rates will therefore remain low if the adoption and use of financial services remain low.

Evidence suggests that several financial services are inappropriately designed and unsuited to the needs of the diverse segments of unbanked and underbanked Nigerians. Additionally, there is an overestimation of the market size owing to lack of adequate market research and R&D, which can cause products to not meet financial projections. Products also fail because of poor product designs stemming from inadequate requirement analysis and a lack of well-designed and tested prototypes before products are launched. This can also translate to wrongly positioned, priced, or advertised products that underperform in terms of adoption and profitability. One reason highlighted by this study is that the different FSPs have insufficient product development skills. Most times, product development teams comprise software engineers and those with a manufacturing background whereas financial products are service-oriented. The absence of a skilled and well diversified team can translate to high development costs, which may lead to unprofitable products ( Olson et al., 2001 ).

Addressing these concerns requires FSPs to reconfigure their product development teams. The teams must possess the capabilities needed for the development of quality financial services (products) that meet validated market assumptions. With the right set of product development capabilities, poor product performance can be closed using industry-wide market research that incorporates human-centred design (HCD) and design-thinking techniques. These can lead to the development of financial services that are customer-centric and widely adopted, trusted by consumers, and compliant with market regulations. Additionally, market knowledge breadth that flows from rigorous R&D can help firms transform novel ideas into new products, thereby intensifying product performance ( Jin et al., 2019 )

Good R&D practices such as the adoption of approaches such as human-centred design can help product managers and the broader product development teams to provide answers that validate initial market assumptions. These could be assumptions on consumer needs, buying behaviour, market share, rival firms, and other prevailing market conditions. These methodologies can help answer questions such as the following: 1) What non-existing value is being proposed by the new product? 2) What use cases exist, and what is the addressable market? 3) Is there an effective product development process? How competent is the product development team? 4) What is the time frame between ideation and launch? 5) How efficient is the operational process? 6) Is the product prototype tested before or after launch? 7) Are there strategies for mitigating risks emerging from new financial product development? 8) What are the feedback channels for customers' opinions on new products? 9) How does management respond to unfavourable feedback on new financial products?

7. Managerial implications

The literature ( Schilke, 2014 ; Pavlou and El Sawy, 2011 ) provides empirical evidence that suggests that the more an organization invests in product development practices, the higher the likelihood of product success. Effective product development practices (PDPs) serve as forms of competitive strategies, especially in an industry with multiple players ( Cheng and Yeng, 2019 ). DC theory argues for the development of capabilities that help a firm meet the demands of a dynamic environment of business. In the Nigerian financial sector, management teams of FSPs must build the capabilities required for product development to ensure the high performance of their products. It should be noted that executive commitment and support are key success factors for both product development teams and ultimate product performance.

7.1. Study limitations and directions for further studies

A limitation of this study is the limited number of respondents, which makes the generalization of the findings challenging. Moreover, the busy nature of the category of respondents (being c-level executives) meant that some were unavailable, and single interviews were sometimes conducted twice to enable the authors to gather rich data. This reduced the number of respondents for some FSPs to one, making it difficult to achieve diversity across functional roles in the product development spectrum and, to a certain extent, leading to potential data loss. Further studies should aim to reach a larger sample size and involve several respondents in a single organization to achieve a higher level of saturation. Future studies can further distil the issues into distinct FSP types such as banks, insurance companies and pension providers in the Nigerian market.

Author statement

All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Technological Forecasting and Social Change .

Biographies

Dr Nkemdilim Iheanachor is a Faculty member in the Strategy and International Business Group of Lagos Business School, Pan-Atlantic University, Ajah, Lagos, Nigeria. Nkemdilim holds a PhD Degree in Management from Pan-Atlantic University.

Mr Immanuel Umukoro is a Research Fellow in Lagos Business School, Pan-Atlantic University, Ajah, Lagos, Nigeria. Immanuel holds a Master of Science Degree in Information Science from University of Ibadan

Professor Olayinka David-West is a Professor of Information Systems in Lagos Business School, Pan-Atlantic University, Ajah, Lagos, Nigeria. Olayinka holds a Doctorate in Business Administration Degree from Manchester Business School.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.techfore.2020.120470 .

Appendix. Supplementary materials

• Newsletters
• Memberships
• Enter your keyword and hit 'Enter'

Sign Into Digital Commerce 360

Forgot your password?

A digital manufacturer IDs ‘need for speed’ in product development

Protolabs finds market competition drives many companies to speed up already expedited product development times..

In a competitive business environment largely freed from the supply chain shortages of recent years, many manufacturers find they must strive harder to develop products quickly.

That’s among the findings of a recent survey of over 700 product engineers, designers and developers by digital manufacturer Proto Labs Inc.

The company’s market research team surveyed product development professionals across such manufacturing industries as aerospace and defense, automotive, medical equipment and devices, industrial equipment and consumer electronics.

Protolabs rebrands its digital manufacturing network

“Respondents made clear today’s product development process is driven by a need for speed,” Protolabs says in the report, “Product Development Outlook 2024: Innovation challenges of today and the future.”

Protolabs provides digital manufacturing services, including 3D printing, injection molding, CNC machining and sheet metal fabrication. Its customers use its services for work ranging from product prototyping to production.

“As a manufacturer serving customers from prototyping to production, we have a front-row seat to watch companies bring products to market faster than ever,” says Luca Mazzei, strategic growth officer.

For now, little help expected from AI

Among the report’s findings:

• While 53% of respondents said they’re developing products “faster than ever,” more than 80% are “looking for ways to be even faster.”
• 65% cite market competition as the primary motivating force behind expediting product development.

The report identified marked improvements in supply chain expectations and the availability of production materials:

• 33% of respondents predicted they would have to deal with materials shortages this year, down sharply from 70% a year earlier.
• 44% predicted material shortages would impact their product development operations over the next five years, down from 74% a year earlier.

The report also revealed how product development professionals expect other trends, including skilled labor shortages and AI, will impact their workload and operations:

• 78% of respondents said the biggest pressure for developing products will come from customers expecting rapid product iteration and modernization.
• 65% cited as a significant challenge a shortage of skilled workers.
• 66% said they expect AI to have “little or no impact” on product development over the next five years before it matures as a technology.
• 63% said they expect sustainability and environmental impact trends will have “little or no effect” on product development.

Paul Demery is a Digital Commerce 360 contributing editor covering B2B digital commerce technology and strategy.  [email protected] .

Submit a nomination

Nominate a game-changer for the  Global B2B eCommerce Industry Awards  from Digital Commerce 360 and the B2B Ecommerce Association.

Sign up for a  complimentary subscription to Digital Commerce 360 B2B News , published 4x/week. It covers technology and business trends in the growing B2B ecommerce industry. Contact Mark Brohan, senior vice president of B2B and Market Research, at  [email protected] . Follow him on Twitter @markbrohan. Follow us on  LinkedIn ,  Twitter ,  Facebook  and  YouTube . 

More on This

In This Article

• Manufacturing
• Consumer Electronics
• Consumer-Brand Manufacturers
• B2B Ecommerce
• Automotive Parts

Related Stories

Digital manufacturer Protolabs scores record quarterly sales

Manufacturer Protolabs builds out its digital services

Digital pioneer Protolabs takes its ecommerce strategy to the next level

Xometry touts AI strategy and record Q3 revenue

• About Digital Commerce 360
• Our Products & Solutions
• Free Subscriptions
• Our Research SHOP
• News & Analysis
• Retail Ecommerce News
• B2B Ecommerce News
• Digital Commerce 360 Blog
• Free Industry Reports
• Charts & Infographics
• Vendor Directory
• Return Policy
• Agreement Terms & Conditions
• Privacy Policy
• Terms of Use
• Website Membership Login
• Database Login
• Connect with Us
• Advertise With Us

Copyright © 2024 Digital Commerce 360 | Vertical Web Media LLC