HR Black Box refers to discussions surrounding how human resource professionals should better design and then articulate what happens between HR concepts and actual improvement in company performance. Discussions of the Black Box are prevalent among industry practitioners and change practitioners. Black box discussions conclude that human resource practitioners should constantly be seeking quantitative methods to support qualitative assessment as part of their overall strategic objectives.
Today, the HR profession relies heavily on qualitative assessments, and the majority of practitioners are not actively seeking methods such as organizational network analysis to support already existing methods in daily operations.
Organizational network analysis itself has much room to improve but it can still be immensely useful to practitioners. Organizational network analysis’ current reliance on survey techniques decreases its reliability and validity for corporate environments because of the challenges in getting a good response rates, and the feeling that “it’s just another survey”. However, there are many tools that are available for use with current enteprise technologies that make it possible to measure network structure much more accurately than with what surveys can provide. The challenge then becomes in Early adopters of this potent social technology must strive to begin the process of using more accurate methods to measure networks, and in so doing will pave the way for even more accurate assessments and enhance value creation for corporate and governmental entities.
However, as the science of network analysis continues its development and alignment to organizational needs, as with any data-driven tool, it must move beyond the assessment of current conditions of the organization, to the prediction of “what if” scenarios—otherwise known as predictive analytics.
The network view of the firm’s true power is similar to its counterparts in statistical sociology: the ability to predict future events, derived from its ability to provide insight into a future state of the company. This advantage is not easily procured with current technologies and methods. The theories already exist, but critical mass in awareness of the method has yet to appear. Coincidentally, the network perspective faces much resistance from practitioners who fear its highly conceptual, analytical, and mathematically based method of analysis. Thus, the perspective itself will have to face challenges before its eventual, but unquestionable adoption by the mainstream in the future. As Roehling and others describe the future of network techniques: “Studies adapting [organizational network analysis] tend to describe the actual patterns of interaction rather than prescribe the organizational work relationships that should be developed. To contribute to HR practice, however, researchers must move beyond the description of the social network to the prediction of a social network.” (Roehling et al., 2005).
Finally, practitioners are still continuing the debate of whether it makes sense to use organizational network analysis for the purposes of change management. The literature on the subject is still largely undeveloped but it is growing in both size and scope. Organizational network analysis tends to be a highly technical discipline which may create discomfort in using the methods themselves for HR professionals. In addition, it is a multidisciplinary study of different social and technical subjects and requires multiple skills to reach full conceptual grasp. It contains statistical and mathematical concepts which for most social scientists deems it a subject that they would not necessarily investigate, but organizational network analysis carries within it a bright promise for the future and hence worth the effort and hard work.
ONA contains a promise that manifests in its ability to quantify that which was considered previously to be un-quantifiable. Data-driven human resources can use such a powerful tool in its cadre of social and technical armaments that will ultimately help to improve organizations across the world.
Organizational network analysis takes the perspective that individuals are not truly individuals, but are part of a larger collective, that they are influenced by others and influence others. Intuitively that perspective seems much closer to the truth than what is currently being offered by traditional social scientists and practitioners in today’s electronic world. This is the key to affecting better change, and bringing forth the value that is promised by the human resource profession—by identifying collaborations and relationships that are currently not visible.
As it regards change management, organizational network analysis offers a network map for your organization that allows you to see the strengths and weaknesses, opportunities and threats that you face, so that you may correct or mitigate them proactively and without need for expensive software or information technology tools—After all, the majority of organizational network analysis methods are based on simple surveys. One can engage a simple survey through the use of paper and a pencil, combined with a data analysis tools such as Microsoft Excel, and you have all the tools that you will ever need to get started on everything networks in your workplace.
Change management theorists concentrate on diagnosing the problem and then providing some method to correct the problem, however often we are unable to diagnose problems efficiently, and thus our solutions provide us with no real answers. In one study, it was estimated that over 40% of all change management initiatives end in failure. Some of the methods and examples that we have implemented and studied can provide for an increased amount of change success, but they must be used diligently and effectively.
The world can no longer be seen as a set of individual actors floating about serendipitously. With the advent of information technology, globalization, and increase in the interconnectedness of society, organizations will become cohesive intelligence, information processing bodies—a collective!
In science fiction stories, such as Star Trek, imaginative writers almost always propose that superior intelligence could always be achieved by artificial intelligence, because this type of intelligence structure is able to pool their information processing powers together for a greater cause. One then must question whether today’s organizations, having become more social, are moving towards a similar paradigm. And although we may not be connected in every moment of every day, already social networking sites such as MySpace, Facebook, Twitter, and LinkedIn garner a considerable amount of peoples’ time.
Humans are social creatures and so they seek to build connections with others regardless of their individual attributes but more by their social behavior. Organizational network analysis allows us to measure that consistent behavior in order to draw conclusions from it to serve some larger goal. For the ONA practitioner, her goals could be as diverse as reducing climate change through human interventions, fighting famine and disease or simply building a better call center.
In my research, no other quantitative method provided a more accurate analysis of organizational contexts.
There is a common proverb that says that 99.9% of all human activity could be categorized as habitual. One way of looking at organizational network analysis is that it attempts to measure those habits rather than measure traits that yield no real insight into human activity, and human reasoning.