In this study we examine the relationship between news media importance of different energy-related themes (ERTs) and the level of electricity generat…

Media reputation plays an important role in attracting venture capital investment. However, prior research has focused too narrowly on general media e…

Family or non-family leader? Your choice depends on the age of the firm, and can ultimately affect how the media and outside stakeholders see you.

In this paper, we draw on the distinction between two types of audiences in the Initial Coin Offerings (ICOs) context (i.e. traditional investors and communities of followers) to explore what kind ...

We investigate how bank-affiliated VCs (BVCs) change their investment strategy in fintech startups relative to independent VCs (IVCs) after the global financial crisis (GFC). To this end, we use the c...

Andrea Fronzetti Colladon - List of Publications.

This study expands upon previous research on family firm leadership by exploring the role of CEO identity – i.e., family vs. nonfamily CEO – concernin…

This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020).…

This chapter confirms and extends the empirical evidence that discusses the role of individual traits in determining resource acquisition by entrepreneurs. Using a sample of 59,538 crowdfunding campaigns launched on the crowdfunding platform Kickstarter, it explores how the use of language can impact a campaign’s success. This chapter adopts a combined methodology that relies on the Crovitz 42 Relational Words, emotional text mining (ETM), and machine learning to model key variables and identify the elements of a campaign that could primarily affect successful funding. This research concludes with practical suggestions for how entrepreneurs can use language and other controllable variables to increase their chances for success with their crowdfunding campaigns from the very start, as well as understand which uncontrollable variables may boost their chances of success during a campaign.

Many companies are facing increased competition within their industry. One way to succeed is to understand potential customers and their needs, to differentiate from competitors with a strong brand personality and a targeted social media presence. User-generated content (e.g., Twitter tweets or Facebook posts) and its language style tell a lot about a person. We can infer feelings, behavior, and personality from this analysis. This chapter uses social media data and machine learning approaches to explore how congruence between customer and brand personality influences a company’s success. We collected and analyzed Twitter data on 29 car manufacturers in the U.S.A. and information on their potential customers. We used IBM Watson Personality Insights and Griffin Tribefinder to compute personality features for each customer and the brand’s “brand personality”. Our results show that brands with a personality similar to their customers are more successful in sales. However, there are also a few traits where customers prefer an opposite brand personality to complement their personality. This study helps marketing managers to implement strategic choices to improve their competitive advantage, aligning new products with the expectations of their customers.