Beta Testing in Social Work

The Conflict Between Innovation and the Scientific Method

During the last 100 years, the field of social work has made great gains in establishing itself as an applied social science. Once viewed as an avocation of friendly visitors, the field was criticized for failing to legitimize itself through engagement in intellectual operations involving individual responsibility, deriving knowledge from science and learning, and applying its knowledge with techniques that are educationally communicable (Austin, 1983). The field has now become inextricably tied to the scientific method and is often at the helm of developing and adapting randomized control trials and other prevention strategies. The scientific method dictates a process of forming hypotheses, deriving predictions from them as logical consequences, and then carrying out repeated experiments based on those predictions. A scientific hypothesis must be falsifiable, implying that it is possible to identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, the hypothesis cannot be meaningfully tested. Scientific methodology often directs that hypotheses be tested in controlled conditions if possible. The practice of experimental control and reproducibility is intended to diminish the potentially harmful effects of the intervention being researched and reduce personal bias. The scientific process is based on demonstrable effect sizes, large-scale trial, and approximation of perfect intervention. However, a common side effect of this arduous approach to scientific advancement and innovation is a lengthy period for diffusion of findings. In the field of social work, the end goal of most scientific advancements is to reduce human suffering, and as such, the sluggish process of innovation development, dissemination, and adoption is a high-stakes game when considering the lives of individuals waiting on potential programs and remedies borne out of rigorous science.

Conversely, other scientific fields such as technology and engineering have adopted beta testing as part of the scientific process. Not only does this accelerate the scientific process and ultimate diffusion of findings but it is also cost-effective. Embedded in such adaptation to the scientific process is the philosophy that it is better to fail early when it costs less to make changes to a finished product or service. Some degree of failure is perhaps paradoxically expected as part of the beta testing process. Accordingly, failure is not viewed as a disappointment, but rather a catalyst for a structured course correction designed to test a new fundamental hypothesis about the product, strategy, and engine of growth (Ries, 2014).

Embracing Failure of Science and Trusting Human Capability

In January 2015, 1,300 artificial intelligence scientists (and two social workers) met in Austin, TX, for the 29th AAAI Conference on Artificial Intelligence to present cutting-edge research on artificial intelligence. A press release highlighted four papers that demonstrated some of the most interesting advances being made in the field. Many people limit their concept of artificial intelligence to what they see in movies or read in science fiction novels. However, the field is quite diverse with many areas of study. One of the goals of the field is to develop smart homes, in which robots in the kitchen help with tasks such as cooking (think Rosie from the The Jetsons). Unfortunately, a robot-ready kitchen is much further off than one may hope. Of the cutting-edge papers included in the press release, the research of Yang, Li, Fermüller, and Aloimonos (2016) from the University of Maryland was highlighted. This team of scientists has developed a computer system that learns how to grasp kitchen objects by watching YouTube cooking videos. To conclude its presentation, the team presented a video of its robot picking a spoon and a bottle of salad dressing off a kitchen counter. After the robot squeezed some dressing into the bowl with one arm, it proceeded to stir with the second. End video. This was a major accomplishment in the field. For the nonartificial intelligence scientist, this will likely sound thoroughly underwhelming. However, as the researchers continued to boast about their accomplishments, they brought up the fundamental challenge of the science of artificial intelligence: Robots can never be quite as excellent as humans.

Humans are extraordinary beings, and where we excel compared to robots is in making decisions in the face of uncertainty. The primary challenge of an artificial intelligence scientist is to develop a program that can imitate this key ability of humans. A simple example is to imagine that you are preparing a meal in an unfamiliar kitchen, perhaps at a friend’s house, and there is bowl and mixing spoon that you have never seen before. You can imagine that very quickly you would determine how to pick up that spoon, how to hold the spoon, and how to use it to mix salad dressing in the bowl. For a robot, it is not as easy. Every item in a kitchen needs to be recognized and the robot must determine an appropriate grasp for each unfamiliar object. This is surprisingly hard for a robot; with every object, a specific grasping decision program must be developed. Therefore, you can imagine how distant the dreams of the robot kitchen assistant actually are. As medical science also has determined, the human is the most complex and effective invention of all time. The sciences will dedicate their efforts in attempt to replicate it until the end of days. However, as a society and in the field of social work, we have to come to a place where we tend to trust science over human capacity. This is where systems such as beta testing come in. Instead of fearing the failure of science (i.e., the failure of evidence-based interventions), we need to embrace it as part of the process, not only for the aforementioned reasons but also because we should trust our unique human capacity to make decisions in the face of uncertainty. Failure is OK because as humans, more so than anything else, we will be able to handle it.

A Proposed Model for Beta Testing in Social Work

For social work to have more flexibility in the scientific method and shorten the innovation timeline, we must leverage beta testing methods from other fields to advance our impact in the world. Failure to implement a process of beta testing in social work means that we will continue to rely on innovation scaling that is politically driven with little evidence behind it, as has happened, for example, in the case of diffusion of wraparound services. Choosing a method of beta testing must be done cautiously, so that we maintain our profession’s key value of beneficence and ensure that our scientific flexibility still confers utmost levels of safety and maximum benefits to our client populations.

Lean startup is a method advanced by Eric Ries (2014) for developing services and products; he proposed that innovators can shorten product development cycles by adopting a combination of business hypothesis-driven experimentation, iterative product releases, and what he calls validated learning. Ries’s overall claim is that if innovators invest their time into iteratively building products or services to meet the needs of early customers, they can reduce market risks and sidestep the need for large amounts of initial project funding and expensive product launches and failures. The lean startup philosophy seeks to eliminate wasteful practices and increase value-producing practices during the product development phase, so that startups can have a better chance of success without requiring large amounts of outside funding, elaborate business plans, or a “perfect” product. Ries (2014) stated that customer feedback during product development is integral to the lean startup process and ensures that the producer does not invest time designing features or services that consumers do not want. Because startups typically cannot afford to have their entire investment depend on the success of a single product launch, Ries maintained that by releasing a minimum, likely viable (but not finalized) product, the company can then make use of customer feedback to help further tailor its product to the specific needs of its customers. This build–measure–learn loop emphasizes speed as a critical ingredient to product development. A team or company’s effectiveness is thereby determined by its ability to ideate, quickly build a minimum viable product of that idea, measure its effectiveness in the market, and learn from that experiment. In translating these ideas and processes into innovations in social work, the lean startup approach aligns well with the key social work values and ethics, particularly those based on self-determination and continuous client feedback.

The field of social work can arguably take several simple steps to implement the lean startup method to catalyze social work innovation. The first step is developing business models designed to search for repeatable and scalable innovative solutions. Compared to a business plan, which details elements of marketing, customer, and business development, a business model is an evidence-based means of describing how a company creates value for itself while delivering products or services for customers (Osterwalder & Pigneur, 2010). The business model incorporates a marketplace view detailing customer relationships, customer segments, communication channels, and revenue streams with an operations view of key partners, key activities, key resources, and cost structures. At the heart of the business model are value propositions, as opposed to product features and benefits. It describes the customer problems being solved and the customer needs being satisfied. In short, the business model is a beta testing planning tool that is client focused, value driven, and facilitates the design of repeatable and scalable solutions to client needs and desires. See Figure 1 for an example of a business model canvas.

OPEN IN VIEWER

Figure 1.

Business model canvas. Adapted from Osterwalder and Pigneur 2010.

Once the business model canvas is complete, beta testing can begin. As in the standard scientific method, applying this approach to beta testing in social work would commence with a hypothesis; however, in this model, the hypothesis being tested is the correlation between value propositions and customer segments. The beta tester will test a minimum viable product or service to assess if their innovation creates a product–market fit, or bridge between what clients desire to improve about their lives and what can be feasibly supported by social work practitioners or social service organizations. The business model canvas serves as an innovation scorecard of sorts, whereby each element can be adjusted until excellent fit is achieved.

More concretely, what could beta testing in social work look like? The business model canvas, in several ways, is akin to conducting a community needs assessment, or mapping of community-based assets and challenges, functions of which many social workers are adept purveyors. However, in adding a component of beta testing to extant approaches to community-engaged social work, community members’ preferences, feedback, and buy-in would be sought earlier in the product development stage. This is important, because researchers’ perceptions and agendas associated with neighborhood-level or population-specific social problems are often in disequilibrium with the actual needs and opinions of in-group members. In such events, products (or rather, proposed definitions of social problems and approaches to solutions) are not crafted with consumer demand in mind. Moreover, reframing community members’ expectations, such that some failure is expected and embedded in this approach to problem-solving, is integral to the tenets of beta testing borne out of the business model canvas. After all, any given social problem would have already been eradicated if solutions were so obvious; but wouldn’t it be better if snags in product development were caught earlier? When left to unravel, community enthusiasm and commitment to civic engagement so frequently dissolve and lead to perceptions that problems are simply intractable. In reframing that beta testing involves trial and (more importantly) error, efforts at solving community-based problems that we are so frequently led to believe are beyond our capacity to ameliorate (e.g., gun violence, pollution and environmental hazards in low-income communities, obesity amid food deserts, reproductive and sexual health disparities in communities of color, etc.) may instead progress, albeit via circuitous, constantly revising paths.

The notion of community stakeholders being active participants in this model is of central importance; for beta testing in social work to be successful, a process of working with and talking to many different people from communities (and other professionals with whom social workers almost never engage, such as business, engineering, information technology, etc.) is crucial. Furthermore, although the underlying causes of most social problems are larger concepts (poverty, racism, sexism, inequality, etc.), social work in some ways is the business of working with outliers. Social workers commonly work with marginalized populations that have been pushed to the fringes of society and are not viewed as mainstream or deserving in some way.

Most business models, conversely, are quite utilitarian and seek to develop products that appeal to as many people as possible, mostly to yield the highest possible profits. The proposed process of social innovation, however, must be granted permission to differently measure its profits and returns to shareholders and thus appeal to more customized problems. In such a model, returns would be viewed as accretive if specific (marginalized) problems are mitigated, even if such products do not directly reach as many people as, for example, iPhones (a highly profitable technological innovation). Accordingly, broader society benefits greatly when problems affecting marginalized populations are solved, but as part of these updated cost–benefit analyses of social innovation, value to the shareholder must be reframed as value to the public good. This is difficult to operationalize, but important to emphasize when considering social work’s application of beta testing.

Standards for scholarship must also shift to encourage and justify beta testing and related social innovation efforts. Funding streams are both competitive and limited in terms of what topics or approaches to research are prioritized or even allowable. Social work researchers are often prompted to change their research questions to fit with rigid funding priorities of large research-granting federal agencies and private foundations. Many researchers thus do not necessarily investigate what they think are the most salient intervention targets, or problems ripe for innovation, but instead, what is seen as the most fundable or “safe” agendas. This phenomenon is reinforced by the broader tenure and promotion structure, which evaluates researchers’ successes based in large part on the amount of funding a given researcher has acquired, especially in the form of federal grants. Although federal grants are deemed the holy grail of funding in our field and are undeniably the source of many important scientific discoveries, alternative funding sources should be regarded by academic institutions as valuable, if not equally important, as we seek to improve client outcomes through science. Moreover, political agendas often dictate that certain lines of research inquiry are deprioritized, or outright banned, from receipt of federal funding. For example, reproductive and sexual health research has seen many examples of this, with federal research funding waning or disappearing altogether depending on the political climate for studies regarding topics such as contraception, family planning, and pregnancy prevention specifically among vulnerable emerging adult populations.

Private (sometimes anonymous) foundations and donors, however, have been responsible for funding innovative approaches to such issues. For example, a program in Colorado used a beta-release strategy of sorts to deliver long-acting, reversible contraceptive devices to women and teens confidentially and free of charge, resulting in dramatic reductions in both unintended pregnancy and abortion rates (Tavernise, 2015). Such a study was not eligible for federal funding, but nonetheless delivered highly successful results in a very expedient time frame following the receipt of funding and commencement of programming. Also, although some topics are not considered fundable, it does not make them nonproblems. Such topics are nonetheless important social issues that are in urgent need of innovation and have arguably been made worse due to over politicization and under- or nonfunding. Social work, in implementing different standards for scholarship, would seem particularly heroic (and would be doing what it thinks it does) if it solved social problems, regardless of the source of funding to do so. The product that social work aims to deliver as a result of beta testing and social innovation is the reduction of suffering and the improvement of life outcomes for people—even if accomplished through avant-garde approaches.

Scholarship standards may also benefit from adopting an updated model of diffusion. The business world arguably accelerates diffusion better than any other sector. For example, the real-time speed at which financial securities research moves is vastly different from the peer-reviewed publishing process. Although the integrity and caution of the peer-reviewed academic research process is admirable and important, there is nonetheless value to disseminating preliminary findings and observations in a time frame that can be counted in days or months, rather than decades, such that the time lag by which research informs social work practice may be dramatically shortened.

If scholarship standards (and the tenure and promotion process) placed increasing weight on researchers’ dissemination of findings through rigorously crafted white papers, blogs, social media, and other quickly consumable modes of information sharing, cutting-edge research could emerge daily, rather than as interesting findings from a few years ago that we are now just hearing about. Furthermore, it is of paramount importance that researchers continually remain present in the communities with which they work and conduct research, because talking about preliminary findings with community stakeholders to get perpetual feedback is the only way to prevent the aforementioned build–measure–learn loop from disintegrating. In conclusion, research is never really done, and findings are somewhat ephemeral—even the ones that are deemed evidence-based practices are nonetheless adapted and modified across time as we learn more. As such, diffusion of results needs to happen with rigor and a degree of caution, of course, but more efficiently and quickly, particularly as technology innovates new ways each day to disseminate and consume more knowledge and ideas.

The Increasingly Altruistic Consumer and Seeking New Funding Streams for Social Good

Numerous factors support the argument that now is the perfect time for social work to harness innovation. Historically, there have been tensions between the fields of social work and business, with social work often critical of profit-driven goals and wealth inequality stemming from corporate greed, and business holding the perception of nonprofits and social service agencies as inefficient, bureaucratic, or lacking a sense of urgency in their development and adaptation of solutions to social problems. These respective critiques may be simultaneously valid and unfair. However, there are notable reasons that these sectors should work more closely in mutual partnership toward ends of social good.

First, there has been an increasing shift in consumers’ propensities to purchase products and invest in companies with socially responsible philosophies. Companies with business models of “buy one, give one” have surged, under the premise that as a consumer buys a product (e.g., a pair of shoes from TOMS, a pair of eyeglasses from Warby Parker, etc.), an equivalent product will be distributed by the company to a person in need. These practices have been scrutinized by both business and nonprofit experts for failing to address root causes of problems (e.g., poverty, inequality, racism) and lacking transparency about where and to whom these charitable offerings are actually disseminated (Townsend, 2014). However, there is nonetheless a palpable consumer appetite for purchasing products from companies with such missions. For example, when private equity firm Bain Capital acquired a 50% stake in TOMS in 2014, the company, less than a decade after inception, was then valued at US$625 million.

Moreover, sustainable, responsible, and impact investing, an approach to market investing that emphasizes corporate governance, environmental, and social criteria to garner competitive financial returns while positively affecting society, is also rapidly precipitating. At the end of 2013, more than US$1 of US$6 under professional management in the United States was invested according to such parameters, totaling more than US$6.5 trillion (Forum for Sustainable and Responsible Investment, 2014). Such investment priorities have been heralded for playing a role in persuading large publicly held companies to improve disclosure practices pertaining to climate risk, address poor human rights and labor conditions in their global supply chains, promote racial and gender diversity among their boards of directors, and pledge to not discriminate against employees on the basis of their sexual orientation, among other positive social outcomes (Forum for Sustainable and Responsible Investment, 2013). In addition, companies engaging in these corporate social responsibility practices have not only demonstrated strong returns on investment to investors but employees of such companies have reported high rates of job satisfaction and commitment to their work (Weinreb, 2015).

As such, social work’s increased emphasis on innovation and beta testing should further leverage these larger market-based trends of socially responsible investing and altruistic consumer preferences, particularly in developing mutually beneficial partnerships with venture capital in the form of new streams of funding for innovating social good. Such concepts have begun to emerge, with social venture capital firms providing incubator or seed funding, often along with facilities and business operations expertise, in attempts to provide requisite resources and infrastructure for new ideas and grassroots-based social innovation experiments to gain traction. The further replication of venture capital and social work partnerships could provide support for potentially innovative research efforts that may fall outside of the often-narrow funding priorities of government agencies and large foundations. With increased involvement of social work innovators at the leadership tables of such social venture capital partnerships, venture capital infusion could be used such that more attention could be paid to the factors that social workers are already the greatest experts at contemplating and seeking to disrupt deeper, systemic root causes of social problems, inequality, and oppression.

The field of social work is ripe for beta testing. Consumers are increasingly familiar with and willing to engage in beta testing. Other disciplines have shown how crucial beta testing is to scale solutions to large problems quickly. New funding streams are becoming available to support innovations that will lead to social change. Most importantly, fields such as business and engineering, in which beta testing is common, are increasingly turning their attention to solving social problems. With social work expertise and involvement also present at the helm of these efforts, social innovation could become all the more successful.