In our first blog, we made reference to the likely causal relationship between data-driven decision-making and the positive effects this has on firm performance (Brynjolfsson, Hitt & Kim, 2011). To this end, the cumulative human capital of data professionals and data-minded employees play a key role in the enablement of data-driven decision-making. Due to the scarcity of data professionals, it is essential that firms manage job satisfaction levels to ensure that they are well-positioned to live up to their potential. In addition, prior studies have shown that higher job satisfaction in general increases firm performance (e.g., Kessler et al., 2020).
Consequently, research on work redesign – alternatively known as job enrichment – as a strategy to improve job satisfaction as an intermediate step to increased levels of firm performance dates back to the 1960s (e.g., Herzberg, 1966). The latter author has developed the motivation-hygiene theory, stating that intrinsic motivation is ultimately what raises job satisfaction levels. Conversely, the so-called hygiene factors, such as financial rewards and pension plans, only act as means in which to prevent dissatisfaction. In other words: financial rewards, for example, must be satisfactory and meet certain benchmarks, but do not play a key role in long-term job satisfaction.
The central question that subsequent research attempted to answer may be posed along the following lines: ‘’How is one able to manage the levels of intrinsic motivation to increase the job satisfaction of its employees and thereby improve firm performance?’’ Consequently, differing authors from various disciplines have provided multiple perspectives to shed light on this strategic issue. These have been encapsulated in theories that focus on aspects ranging from the extent of cognitive stimuli (e.g., Berlyne, 1967) to the interdependencies between technical aspects of work and the broader context in which it is carried out (e.g., Emery & Trist, 1969).
Ultimately, the job characteristics model devised by Hackman and Oldman in 1976 has emerged as the predominant theory of job enrichment. According to their research, there are five core job dimensions, such as skill variety, that jointly impact three critical psychological states. Improved psychological states, such as the experienced meaningfulness of the work, subsequently heightens the levels of employee motivation and satisfaction, increases the quality of work, and lowers the rates of absenteeism and turnover. The rest of this blog delves further into these core job dimensions and provides practical suggestions on how these may be enriched for data professionals.
Job Characteristics Model
As stated previously, the psychological states lie at the heart of the job characteristics model. In another article co-authored with Lawler (1971), Hackman postulates ‘’that an individual experiences positive affect to the extent that the learns (knowledge of results) that he personally (experienced responsibility) has performed well on a task that he cares about (experienced meaningfulness)’’. The underlying assumption is that this leads to a reinforcing cycle for employees in which increased effort leads to higher rewards. Consequently, the intrinsic motivation of employees is at its highest when all psychological states are managed well through the five core job dimensions.
The first critical psychological state concerns the experienced meaningfulness of the work, which is impacted by three core job dimensions: skill variety, task identity, and task significance. Firstly, skill variety is defined as the degree to which a job requires the use of a number of different skills and talents of the person. Secondly, task identity revolves around the degree to which the job requires completion of a whole and identifiable piece of work. Thirdly, the core job dimension of task significance focuses on the degree to which the job impacts of lives or work of co-employees or other people.
In our daily conversations with candidates, we have noticed that the degree of skill variety may be experienced as either too low or too high. Some data professionals, for example, note that they are expected to stick to coding and modeling activities (hard or technical skills) and should refrain from getting too involved in other aspects, such as stakeholder communication (soft skills). This may result in skill variety being experienced as too low. Conversely, others mention that they are expected to cover complete project cycles by themselves. Consequently, the latter group may experience the expected skill variety as too stretched.
In essence, task identity is tightly coupled to the extent to which data professionals feel that they are responsible for doing a job from beginning to end with a visible outcome. With regard to this core job dimension, we notice two issues in the interaction with candidates. Firstly, data professionals that are employed by relatively larger firms commonly experience a lower level of task identity due to extensive specialization and tasks and responsibilities that are scoped too narrowly at times. Secondly, data professionals are often far removed from visible outcomes resulting from the highly abstract nature of the corresponding activities.
The priorly mentioned aspect also decreases the experienced levels of task significance, as the impact of models devised by data professionals located at the corporate headquarters may be quite distant from the effects that these models have on frontline personnel. In addition, we notice the structurally increasing demand for jobs that allow data professionals to make their mark on society by deploying advanced analytics and their skills for the common good (e.g., positions within healthcare organizations and NGOs). Along similar lines, firms that are more active in terms of corporate social responsibility may also find it easier to attract talent.
The second critical psychological state revolves around the experienced responsibility of the outcomes of the work, which is impacted the core job dimension of autonomy. The job dimension of autonomy is defined as ‘’the degree to which the job provides substantial freedom, independence, and discretion to the individual in scheduling the work and in determining the procedures to be used in carrying it out. If autonomy is deemed to be high, an employee is likely to feel a heightened sense of responsibility towards the outcome of his or her activities. This then hypothetically leads to increased levels of intrinsic motivation.
As with other core job dimensions, candidate engagement reveals that autonomy may be experienced as either too narrow or too stretched. On the one hand, too narrow levels of autonomy may result from strict mandates which force data professionals to work with legacy solutions that do not allow for the extensive adjustments to the existing infrastructure and processes of organizations that may be required to implement newer alternatives. On the other hand, being a lone data professional in a large company unfamiliar with data-driven decision-making may imply that you are left with little guidance on how to effectively conduct projects.
Knowledge of results
The third critical psychological state concerns knowledge of the actual results of the work activities, which is impacted the core job dimension of feedback. This fifth core job dimension is defined as ‘’the degree to which carrying out the work activities required by the job results in the individual obtaining direct and clear information about the effectiveness of his or her performance. To this end, it is essential that data professionals receive proper feedback in order to engage in what is known as deliberate practice. For many data professionals, feedback is an important element in the evaluation of potential employers’ suitability.
Due to the rapidly evolving of business intelligence and data science, candidates frequently note that they value feedback from peers to improve themselves and in order to stay up to date with the latest technological developments. This may involve both interim feedback and feedback that is provided when a project has been concluded. Junior data professionals may especially value the presence and feedback of talented peers and data professionals with longer tenures. As noted by Provost and Fawcett (2013), firms that attract top talent may find it easier to attract highly skilled data professionals in the future – and vice versa.
At risk of stating the obvious, the job characteristics model – like all models – is not without its flaws. As indicated by the authors, the strength between the relations and its causal effects on job satisfaction may differ per mediating variables, such as the desire of employees to grow in terms of skills and knowledge. Due to the human nature and the wide range of inherent and changing preferences, additional mediating variables have been introduced by prior studies (e.g., Boonzaier, Ficker & Rust, 2001). Moreover, other researchers have noted issues in terms of validity and predictive performance (Fried & Ferris, 1987).
Nonetheless, the job characteristics model remains a useful starting point to engage in productive dialogue for employees and firms alike. In engaging with employees, the job dimensions may a useful template for firms to be used when managers attempt to discern the reasons that undergird the particularly high or low levels of the job satisfaction of data professionals. Moreover, such data professionals may find the job dimensions useful in their selection of potential employers and the corresponding trade-offs. One may, for example, for a firm with high expected levels of task significance, even though the opportunities for feedback are limited.
As for managers looking to raise the intrinsic motivation of data professionals, the five core job dimensions provide an actionable set of levers with which to do so. Firms looking to hire junior data scientists, for example, may do well by opting to first hire a seasoned professional that is able to guide and coach them throughout their project and increase the opportunities for feedback that are available to them. In addition, firms could increase the number of occasions in which corporate data professionals interact with frontline personnel or its end users to learn about how their activities impact others.
Feel free to reach out to us at Broadwick to discuss how the job characteristics model and the current design of the corresponding job dimensions affect your firm’s ability to attract data professionals. Although the opportunities to do so are limited only by creativity, we would like to inform you about best practices and actionable opportunities on how to redesign these and offer enriched jobs to data professionals. The same also applies if you are a data professionals that feels like one or more of these job dimensions is highly important to you, yet insufficiently present at your current employer.