|This post is PART 2/6 of a paper entitled “Origins and Progression of Jobs Theory.” Summary—It is argued that ambiguity around customer needs and customer value is the root cause of innovation failure; jobs theory is built on prior theories of customer behavior; how JTBD became bifurcated creating two schools of thought; the two schools are synthesized to create the JTBD framework; post synthesis, JTBD concepts are expanded and refined.|
The Genesis of Jobs Theory
The theory of jobs-to-be-done (aka: jobs theory & jobs-to-be-done theory) provides a lens through which the causality of customer choice can be understood and therefore predicted. This is useful because providers that have foresight into customer choice are able to create products and services that customers will want to buy and use. Further, jobs theory enables companies to identify all competing solutions from the customers’ perspective.
This makes it possible to differentiate or tailor solutions in a way that positions those offerings as the best value among competing alternatives. Simply put, innovation becomes predictable. Jobs theory is also useful for aligning different organizational functions around a common language of customer behavior which can increase the efficiency and effectiveness of cross-functional innovation efforts.
The theory of jobs-to-be-done was developed by the Clayton Christensen, management consultant, entrepreneur and long-time Harvard Business School professor. He and Michael Raynor first wrote about customer “jobs” in the book “The Innovator’s Solution” published in 2003 (1). Christensen and his colleagues continued to expand and refine the theoretical underpinnings of the jobs-to-be-done approach over the years in a series of articles, books and videos.
It is important to note here that when jobs theory is attributed to Christensen, this attribution includes co-authors, researchers, consultants and special contributors like Bob Moesta who have all worked with Christensen over the years to shape jobs theory.
The genesis of jobs theory started some years ago when Christensen began investigating innovation efforts within companies and the extent to which those efforts were successful. His research revealed that 60% of new product innovation projects are abandoned before even making it to the market. Of the new products that are introduced, 40% of those fail to become profitable and are consequently withdrawn. Taking both of these cases into account means that roughly 75% of innovation efforts do not succeed (2). The question that Christensen wanted to answer was simply—why?
Christensen recognized that the aim of innovation is to profitably satisfy the important and unmet needs of a group of customers better than competing solutions at a price they’re willing to pay. Given the reality that only one in four innovation efforts actually succeed, Christensen concluded that there’s something wrong with how companies define customer needs and how they segment customers for the purpose of innovation. Getting these two steps wrong throws off the aim of innovation, which explains why so many innovation efforts are doomed from the outset.
Christensen observed that the prevailing approaches for defining customer needs involve—
1) Asking customers directly what features and benefits they want in solutions;
2) Observing customers in various contexts to ascertain the problems they are trying to solve;
3) Mapping customer activities while using a particular solution to identify pain points; and
4) Correlating the demographic, psychographic and behavioral attributes of the customers’ themselves to infer specific features and benefits that customers might value in a solution.
As Christensen saw it, the problem with these approaches is that the primary focus is on customer and solution attributes rather than why customers buy and/or use solutions. While each of these approaches can reveal insights about customer needs, those insights are often derivative and piecemeal indicators of something more fundamental at work.
An apt metaphor is that of 10 blind men trying to describe an elephant, where the elephant represents customer behavior. Needs defined around the attributes of customers and solutions are ambiguous and incomplete providing inaccurate targets for the purpose of innovation.
Christensen noticed that the way companies segment customers is also problematic. The traditional approach to market segmentation is to group customers according to similar characteristics, that is, their demographic, psychographic and behavioral attributes (i.e., attribute-based segmentation). The assumption is that customers with the same or similar attributes will have similar needs. As such, they’ll want the same or similar features and benefits in a solution.
However, Christensen found that customer attributes by themselves are often poor predictors of customer choice because they do not always explain why an individual chooses to buy and/or use a particular product/service versus a competing alternative. While customer attributes may correlate to some extent with customer choice, they cannot really predict those choices. In short, customer attributes alone are insufficient criteria for designing successful new products and services.
Christensen concluded that companies were missing something vital around which customer needs and segmentation criteria should be aligned, namely the causal mechanism of customer choice. If this causal mechanism were known, companies could precisely define customer needs, accurately target customers with those needs and then design solutions that profitably satisfy those needs better than competing alternatives. Innovation would become predictable rather than hit or miss. Christensen then set his intentions on building a theory that could explain the causality of customer choice (1, 3, 4).
After years of work and collaboration with others, Christensen puts forth the tenets (claims) of jobs theory. It should be noted that Christensen does not articulate these tenets in any single source. Rather, the following tenets are spread throughout multiple publications and recorded presentations—
Individuals and organizations (customers) have lots of jobs they are trying to get done with the aim of making progress in their lives and business; progress can have functional, emotional and social dimensions; customers “hire” products and services (solutions) to help them get those jobs done.
Customers are always trying to get any job done well under a particular set of circumstances (aka: job circumstance or job context). For this reason, a job cannot exist apart from circumstance. Getting a particular job done well means getting that job done as good as possible (or as expected) given the importance of the job and the trade-offs that customers are willing to make with respect to the efficacy of solutions.
Circumstance affects the progress customers want to make and how they want to make that progress. Given that customers hire solutions to make progress, circumstance influences the value that customers want from solutions to get those jobs done well.
A customer “struggles” (aka: moments of struggle) to get a job done when a particular solution-in-use does not sufficiently accommodate or resolve job circumstance, thereby impeding the customer from getting the job done well or getting the job done at all.
When a customer reaches a certain threshold of struggle using a particular solution, the customer will “fire” that solution and hire a competing solution that can get the job done better, faster, and/or cheaper (aka: switch).
Customers buy/use solutions that they perceive can best accommodate or resolve job circumstance in order to get jobs done well. Therefore, the progress that customers are trying to make in a particular circumstance is the causal mechanism that explains, and therefore predicts, why customers choose a particular solution over competing alternatives.
Given these claims, Christensen suggests that—
The customers’ job-to-be-done is the core or master construct that relates desired progress, job circumstance, and moments of struggle, which together explain customer choice. Therefore, the customer job should be the primary unit of analysis (the central focus) for innovation work rather than the characteristics of the customers themselves (as the primary criteria for customer segmentation).
Further, innovators should first understand the job that customers are trying to get done and the value those customers want from solutions to get the job done well before ideating on solution features and benefits.
Innovation efforts informed by jobs theory have will have a much higher success rate because innovators know in advance the value that customers want to get jobs done better, faster, and/or cheaper than competing alternatives. With this foresight, innovation becomes predictable rather than hit or miss (5, 6).
Means-End Theory as the Forerunner of Jobs Theory
It is important to note that Christensen did not create jobs theory in a vacuum. All theories are built from other theories to one extent or another. As a consummate researcher, Christensen was well aware of the diverse scholarly work in the areas of marketing, psychology and economics relating to his research question—why do customers make the choices they do to buy/use certain products and services vis-à-vis competing alternatives?
While Christensen’s work was certainly informed by these areas to some extent, I argue that the core of jobs theory is built on a stream of scholarly research known as means-end chain theory (or simply means-end theory). To show that this is the case, means-end theory is first summarized. Jobs theory is then discussed and compared to means-end theory.
The origins of means-end theory dates back to the late 1970s with the seminal work of two marketing scholars—Jonathan Gutman and Thomas Reynolds. Means-end theory itself is informed by expectancy value theory (cognitive psychology) which was developed in the 1950s and 1960s in an effort to understand what motivates individuals to achieve goals. Building on expectancy value theory, Gutman and Reynolds endeavored to explain why customers choose to buy/use certain solutions over competing alternatives to achieve their goals (7, 8).
Initially, Gutman and Reynolds were interested in understanding how customers associate the attributes of products/services (aka: features) to the end goals they are trying to achieve via the use of those solutions. If this were known, companies could increase advertising effectiveness by highlighting these associations thereby increasing the attractiveness of their products/services.
Other scholars such as Robert Woodruff and Sarah Gardial went further. They focused on applying means-end theory to explain how customers perceive the value of solutions and the factors that drive satisfaction with respect to the use of solutions (9).
Broadly speaking, means-end theory is useful for understanding—1) the criteria that customers use to determine the relative value of solutions and 2) why the level of importance among those criteria differ across various use situations (aka: use context). The fundamental premise of means-end theory is that customers buy solutions for a particular use context to achieve wanted end goals or results (problem solving).
Customers think about problem-solving in terms of how the immediate consequences of solution use can help them achieve their end goals. That being the case, a solution is attractive to the extent customers perceive a strong association between a solution’s attributes and the end goals they are ultimately aiming to achieve.
To put a finer point on it, consequences (or simply outcomes) are what customers immediately experience as they interact with products/services. These outcomes are relevant to customers because they are causally linked in the customer’s mind to wanted end goals. Results are functional (or tangible) in nature, but results can also have and psychological (emotional and social) aspects or dimensions. Further, end goals can be results that customers want to happen and results that customers want to avoid (potential hazards).
The perceived causal linkages between a solution’s attributes, the immediate outcomes of solution use, the context in which the solution is used (use context), and the wanted end goals customers are aiming for comprise a hierarchical chain. The chain represents a hierarchy because the effects of solution use are at different levels of abstraction over time—a cause and effect chain. That is, customers perform actions (activities) which then cause a number of immediate outcomes (the intermediate effects of actions). As intermediate effects, these outcomes are more abstract than the actions that produced them.
The customers’ expectation is that outcomes of solution use will collectively help them achieve their wanted end goals (both wanted results and avoiding unwanted results). End goals as final effects are more abstract than the outcomes that generate them. This is called a means-end chain because customers view solutions as a means to an end. In their book “Know Your Customer,” Robert Woodruff and Sarah Gardial refer to a “means-end chain” as a “value hierarchy.” The conventional means-end model is depicted as Figure 1 (adapted from “Know Your Customer,” p. 233).
For the purposes of juxtaposing the means-end model and the jobs theory model, the conventional means-end model is re-oriented into a horizontal value hierarchy (see Figure 2). Doing so does not change the causal relationships in the conventional means-end model.
Means-end theory suggests that customers orchestrate a number of discrete actions (that is, separate and compartmentalized activities) to achieve their end goals, which characterizes purposeful behavior. Although not shown, discrete activities are implied in the means-end model because the use of any solution necessarily involves performing activities (by definition).
When any discrete activity is performed using a particular solution, immediate outcomes (aka: consequences) occur due to customer-solution interactions. The outcomes that customers want to happen are called desired outcomes. The difference between desired outcomes and actual outcomes determines a customer’s satisfaction with any solution.
For example, say that a particular activity takes 30 minutes to perform which is the actual outcome of performing that activity. A second activity involves an individual explaining a problem he/she is having with a particular service to a support person. However, the support person does not understand how to resolve the problem and the chat session is terminated.
The immediate outcomes of performing both activities are unsatisfactory to the individual. The individual expects to perform the first activity in well under 30 minutes. For the second activity, the individual expects that the support person will know how to resolve their problem. The difference between the desired outcomes (what is expected) and the actual outcomes results in dissatisfaction.
Because customers are performing multiple discrete activities in a means-end chain, they have one or more desired outcomes associated with performing each of those activities. Customers achieve wanted results, as expected, to the extent that the outcomes associated with using a particular solution are capable of generating those results.
Re-organizing the conventional means-end chain model yields a comprehensive rendition that makes explicit the relationship between discrete activities and the outcomes that occur as those activities are performed (see Figure 3). It should be noted that this comprehensive model does not alter means-end theory since this relationship is already implied (but not shown) in the mean-end model.
Therefore, there is a set of desired outcomes associated with any means-end chain. These desired outcomes are the criteria that customers use to judge the efficacy of solutions. As such, desired outcomes determine how satisfied customers are with the use of any solution. Stated another way, customers evaluate a solution based on how well the features of that solution can produce desired outcomes, which are causally linked to the results that customers want. For this reason, a solution is valuable to the extent that it satisfies the desired outcomes with respect to a means-end chain.
Means-end theory suggests that the context in which a solution is used influences the perceived importance of desired outcomes. Outcomes that are more “mission critical” for generating wanted results in a particular use context are more important than outcomes that are less mission critical in that context. For this reason, customers choose solutions that are better suited for a particular usage context because those solutions will better satisfy important desired outcomes.
Therefore, the perceived value of a solution can only be defined by the specific context in which customers are currently using or intend to use that solution. Further, the usage context can change affecting how well a solution can satisfy important outcomes which, in turn, affects the perceived value of that solution. Because the importance and satisfaction of desired outcomes can change over time, customer value is a dynamic concept (9).
Practitioners of means-end theory use an in-depth, one-on-one interviewing method called “laddering” to elicit means-end chains. Laddering involves asking respondents a series of “why is that important to you” questions regarding the use of a particular product/service. The goal of laddering is to ascertain sets of linkages between the attributes of a solution, the immediate desired outcomes of using that solution and the wanted results customers are ultimately trying to achieve.
With this understanding, practitioners can surface the motivating reasons behind brand choice. Sets of linkages or ladders from all interviews are graphically represented in a tree diagram called a hierarchical value map that represents how customers think about a particular product/service category (7).
Even though means-end theory has been around since the late 1970s and continued to develop in the 1980s, it’s adoption among innovators has been very low for several reasons—1) means-end theory was initially developed to inform effective advertising strategies and was therefore not understood as a tool for innovation, 2) the application of means-end theory involves a lot of effort around data collection, coding and analysis using a number of sophisticated methods and tools requiring an advanced knowledge of statistics, and 3) much of means-end theory research is either proprietary or is published in academic journals which has restricted public access to this body of knowledge.
1. Christensen, C. M., & Raynor, M. E. (2003). The Innovator’s Solution: Creating and Sustaining Successful Growth (1st ed.). Harvard Business Review Press.
2. Christensen, C. M. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Boston: Harvard Review Press.
3. Christensen, C. M., Cook, S., & Hall, T. (2005). Marketing Malpractice: The Cause and the Cure. Harvard Business Review, 83(12), 74-83.
4. Christensen, C. M., Anthony, S. D., Berstell, G., & Nitterhouse, D. (2007). Finding the Right Job for your Product. MIT Sloan Management Review, 48(3), 38-47.
5. Christensen, C. M., Hall, T., Dillon, K., & Duncan, D. S. (2016). Competing Against Luck: The Story of Innovation and Customer Choice. Harper Business.
6. Christensen, C. M., & Moesta, B. (2016). Know the Job Your Product was Hired for. Harvard Business Review Digital Articles, 2-4.
7. Gutman, J. (1997). Means-End Chains as Goal Hierarchies. Psychology & Marketing, 14(6), 545-560.
8. Reynolds, T. J., & Olson, J. C. (2001). Understanding Consumer Decision Making: The Means-End Approach to Marketing and Advertising Strategy. New York: Psychology Press.
9. Woodruff, R. B., & Gardial, S. F. (1996). Know Your Customer: New Approaches to Understanding Customer Value and Satisfaction. Malden: Blackwell Publishers.