Text/ Professor Chen Yanzhen
With the pervasiveness of social media, such tools are becoming more and more important in our daily lives, and they will also have an inevitable impact on people's professional development. In a first-of-its-kind study, Professor Yanzhen Chen of the Hong Kong University of Science and Technology and colleagues explored whether building a personal "brand" on social media actually improves executives' career development in the job market?
We conducted this research back in 2014, when Obama was using Twitter as one of his main campaign vehicles, and we were curious whether the same strategy could be applied to business elites. With the rise of social media platforms, job seekers now have a powerful tool that they can use to advance their careers by building a "Personal Branding" that consciously and durably shapes their identity in the eyes of others, such as employers. Personal image.
However, there must be a program in the wood of the ruler, and this method also has certain defects. Even though practitioners often extol the benefits of personal branding, poor implementation can create distaste from potential employers and hurt a person's advancement in the job market. To fill a gap in the literature, the team decided to explore the impact of executives' social media branding on employment outcomes.
To do this, the team of researchers first screened the accounts of verified Twitter executives and obtained information about their personal brand operations by browsing their posts. This information was then combined with executive employment and compensation data from 2010 to 2013. Using natural language processing technology, we measured posts from accounts related to self-marketing and quantified their impact by their follower count. Technological upgrades to the Twitter app have allowed the research team to tease out the impact of self-marketing on labor market competitiveness from a range of characteristics, such as candidate personality.
And the results were quite surprising. We found that a highly active personal brand operation can make candidates more likely to be hired than when they are less active, and operating a personal brand can increase the chance of landing a dream job by more than 30%. As expected, effective self-marketing on social media was more attractive to employers than other characteristics of executives, including their compensation and experience.
Twitter essentially provides a free personal branding platform for job seekers, and users who provide high-quality content for free are likely to benefit the most from this feature. This "balance point of two-way freedom" reflects a win-win situation for active users of Twitter and personal brand operations. Other social media platforms, including LinkedIn, could also benefit from adopting Twitter's business model and unlocking the power of personal brands.
This is the first study to examine the impact of personal branding on job development and offers timely insight for professionals looking to improve their job prospects in today's social media age . The paper could also open up a series of exciting and far-reaching research. It seems interesting and practical to study how personal branding differs for different types of candidates. This will also benefit other players in the job market.
*Reposted from the public account [Hong Kong University of Science and Technology Business School HKUST]
Profile of Professor Chen Yanzhen
Professor Chen Yanzhen, Assistant Professor of the Department of Information, Business Statistics and Operations, HKUST Business School, Professor Chen's research focuses on the application of artificial intelligence in transforming financial analysis and gaining deeper insights. Current projects include empirical research in corporate finance and behavioral asset pricing. She conducts research from a combination of theoretical and empirical perspectives, using theories of IS, game theory, and financial economics to understand people's behavior. Test these theories by applying econometric, Bayesian statistics, and machine learning methods to big data.
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