Service productivity: A call for action!

This article was published in SERVSIG's News letter 28th May 2021

Since the crawling out period of service marketing in the early 1980s (Fisk, Brown, and Bitner, 1993), service researchers have informed policymakers and leaders with vital insight enabling them to advance societies and organizations. 

We have developed seminal knowledge related to service quality, e.g., defining it (Grönroos 1984), measuring it (Parasuraman, Zeithaml, and Berry 1985), and linking it to firms' performance (Rust, Zahorik, and Keiningham 1995). Return on Quality became a term.

In recent years Vargo and Lusch (2004) have convinced us that "everything is service". In addition, we have seen seminal research related to transformative services (Anderson et al 2013), new business models (Benoit et al 2017), automating the front-end with robots and technology (Wirtz 2016), and the role of Artificial Intelligence (Rust and Huang 2019). High tech/high touch became a term.

Nevertheless, with emerging success comes new challenges: Service productivity was much lower than goods productivity (Brynjolfsson 1993, 1994). Sadly, the picture has not changed. Recent OECD data for Norway, which are not unique, indicate a change in total factor productivity (capital, labor, energy, materials, etc) hovering around 0% for the last four years. OECD predictions for the Eurozone toward 2022 regarding labor productivity development is 1,036 %. Krugman (1994) elegantly expressed concern about the severity of this "lack of productivity": 

 

“Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.” 

Where did we go wrong?

Well, we did not. It was destined to happen, and now we must find a solution. As economies transformed from agriculture and manufacturing to service, the transfer of capital and labor followed suit. In the process, we contracted “the Baumol's cost disease” (Baumol 1966), i.e., the rise of salaries in jobs that have experienced no or low increase in labor productivity, in response to rising salaries in other jobs that have experienced higher labor productivity growth.

With the growth of services and the decline of manufacturing, resources gradually transferred from high-productive sectors to low or negative-productive sectors. For example, in the USA productivity from 2005 to 2018 was 1,3%, down from 2,8% from the previous period 1995 to 2004. In OECD, 29 out of 30 countries saw the same pattern after 2004. This is a «Houston, we have a problem» situation!

Measuring productivity and digital services

According to the US Bureau of Labor Statistics, we measure productivity by comparing the number of goods and services produced with the input, which were used in production. Labor productivity is the ratio of the output of goods and services to the labor hours devoted to the production of that output. Consequently, a change in productivity ΔP = ⨐(ΔOutput/ΔInput). Output = Units sold x price per unit. Input = Labor x wages. 

From this, we can draw that productivity increases when firms can produce the same output with less Input, or more output can be produced with the same input or combinations thereof. 

It may be true that service sector productivity is low or negative. However, it may also be due to the transactional nature of services and a measurement problem related to the advent of new and higher-quality ICT goods and services. Reflecting the latter, Robert Solow, a Nobel laureate in economics, commented:  “You can see the computer age everywhere but in the productivity statistics.”

Parasuraman (2002) was the first service researcher who identified a measurement problem with productivity when he elegantly pointed to customers’ input in service production. With customers as co-creators of value, total productivity is, in his argument, a function of firms' and customers' joint productivity. When organizations outsource production tasks to customers, they reduce their own input for the same output. 

Nevertheless, customers, who have to increase their input (i.e., time and effort) for the same output, may experience a drop in productivity and customer satisfaction. Building on the dynamic two-sidedness of productivity, Andreassen, van Oest, and Lervik-Olsen (2017) found, contrary to contemporary thinking, that firms can benefit from a short-term drop in productivity, i.e., an increased input, if customers experience an increase in productivity, e.g., reduced input.

A second measurement challenge is related to social media where consumers are increasingly using free software like Facebook, Instagram, and Google. While this is fantastic, no transaction has taken place and no value is registered. However, value is created. Brynjolfsson et al (2019) responded to this and developed a new measure - GDP-B (for benefits) and estimated that including the welfare gains from Facebook would have added between 0.05 and 0.11 percentage points to GDP-B growth per year in the US. Brynjolfsson, Benzel, and Rock (2020) offer compelling reasons why capturing and measuring value creation in a digital economy is hard:

• With the excitement of technologists and investors, today’s advances simply fall short and will never fulfill their expected economic promise
•  New technologies take time to diffuse, be implemented, and reach their full economic potential
• The technologies are delivering, but we are failing to measure the growing output of the new economy properly
•  the explosion of free digital goods, and that is leading to systematic and increasing shortfalls in our tallies of economic activity.

A call for action

The above raises numerous questions: How do we account for customers' input, how do we set the value of their time, what is the value of ICT in creating value rather than merely reducing costs, and what are the most important capabilities that organizations need to add?

As researchers, we need to develop a deeper understanding of digital value creation, innovation, business models, and transformation for sustainable growth. In my view, this is the roadmap to better measurement of productivity and value creation in a digital service economy. 

Today, services make up 80% of most modern economies’ GDP. For this reason alone, we need to see and measure value creation through new lenses. To quote Albert Einstein «Insanity is doing the same thing over and over again and expecting different results. » Bottom line: The future of our children's children is dependent on their grandparents solving the productivity challenge today! There is no time to lose.

References

·         Andreassen Tor.W., van Oest R.D., Lervik-Olsen L. Customer Inconvenience and Price Compensation: A Multiperiod Approach to Labor-Automation Trade-Offs in Services. Journal of Service Research. 2018;21 (2):173-183

·         Anderson, Laurel, Amy L. Ostrom, Canan Corus, Raymond P. Fisk, Andrew S. Gallan, Mario Giraldo, Martin Mende, Mark Mulder, Steven W. Rayburn, Mark S. Rosenbaum, Kunio Shirahada, Jerome D. Williams, Transformative service research: An agenda for the future, Journal of Business Research, Volume 66, Issue 8, 2013, Pages 1203-1210

·         Baumol, William J.; Bowen, William G. (1966). Performing Arts, The Economic Dilemma: a study of problems common to theater, opera, music, and dance. Cambridge, Mass.: M.I.T. Press

·         Benoit, Sabine, Thomas L. Baker, Ruth N. Bolton, Thorsten Gruber, Jay Kandampully, A triadic framework for collaborative consumption (CC): Motives, activities and resources & capabilities of actors, Journal of Business Research, Volume 79, 2017, Pages 219-227

·         Brynjolfsson, Erik, The Productivity Paradox of Information Technology: Review and Assessment, Center for Coordination Science, MIT Sloan School of Management, Cambridge, Massachusetts, Communications of the ACM, December, 1993; Japan Management Research, June, 1994

·         Brynjolfsson, Erik Avinash Collis W. Erwin Diewert Felix Eggers Kevin J. Fox (2019), GDP-B: ACCOUNTING FOR THE VALUE OF NEW AND FREE GOODS IN THE DIGITAL ECONOMY, NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 

        BRYNJOLFSSON, ERIK, SETH BENZELL, DANIEL ROCK (2020) Understanding and Addressing the Modern Productivity Paradox, MIT WORK OF THE FUTURE

·         Bureau of Labor Statistics: https://www.bls.gov/lpc/faqs.htm#P01

·         Fisk, Raymond P., Stephen W. Brown, Mary Jo Bitner, Tracking the evolution of the services marketing literature, Journal of Retailing, Volume 69, Issue 1, 1993, Pages 61-103,

·         Huang M-H, Rust R, Maksimovic V. The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI). California Management Review. 2019;61(4):43-65

·         Grönroos, C. (1984), "A Service Quality Model and its Marketing Implications", European Journal of Marketing, Vol. 18 No. 4, pp. 36-44

        Krugman, Paul , The Age of Diminished Expectations, Third Edition, 1997, MIT press

·         OECD data: https://stats.oecd.org/Index.aspx?DataSetCode=PDB_GR

·         Parasuraman, A Parsu & Zeithaml, Valarie & Berry, Leonard. (1988). SERVQUAL: A multiple- Item Scale for measuring consumer perceptions of service quality. Journal of retailing.

·         Parasuraman, A Parsu. (2002). Service quality and productivity: A synergistic perspective. Managing Service Quality.

·         Rust, Roland T., Anthony J. Zahorik, and Timothy L. Keiningham. "Return on Quality (ROQ): Making Service Quality Financially Accountable." Journal of Marketing 59, no. 2 (1995): 58-70

·         Vargo SL, Lusch RF. Evolving to a New Dominant Logic for Marketing. Journal of Marketing. 2004;68(1):1-17

·         Wirtz, Jochen Service Marketing People, Technology, Strategy (2016), World Scientific Publishing (US)