Ethical Decision-Making: The Role of Self-Monitoring, Future Orientation, and Social Networks

Abstract

This study examines the influence of individual factors (self-monitoring, temporal orientation) on social networking, and their relationship with unethical decision-making. The study used surveys to measure the unethical intentions and social network data of 129 professionals. Data were analyzed using confirmatory factor analysis and structural equation modeling. The findings provided evidence that individual factors influence the development of social networks and, along with self-monitoring, the likelihood of unethical decision-making. In particular, being in positions of lower network centrality increased individuals' risk of unethical intention. One explanation stems from the need for high situation control to reduce risk and ensure the success of an event, which only a closed network can provide. However, ethical low self-monitor women were also found to have low centrality, so social networks alone do not explain ethical decision-making. This research represents a step forward in our understanding of ethical decision-making through the adoption of multiple and simultaneous factors, proposing an integrated theory of individual and situational factors influencing unethical options.

Wicked webs: business partner networks and silence

“…Moreover, Santos et al (2013) found, that women were less related to corruption -in line with other researches (e.g. Bon et al, 2017), but at the same time have less propensity to report unethical acts. Impressive also are the findings of Behrens (2015) who suggested cultural aspects can even bring drawbacks (unethical use of) whistleblowing such as maligning other professionals.…”

“…Corporations should be concerned about densely closed networks which are relatively isolated from the rest of the whole group within the firm, and at the same time maintain individuals who function as brokers, connecting their network to others firms (Brass et al, 1998;Greve, Palmer, & Pozner, 2010). Tightly closed internal social networks could be broken and/or controlled by means of matrix organizations and more mobility between functions, avoiding groups connected as "honor among thieves" (Bon et al, 2017;Brass et al 1998). However, gender differences in networking are fundamental to be analyzed as some studies have concluded that men and women build different patterns of networks: women have much more closed networks than men (Ibarra, 1997;Burt, 1998), but at the same time some studies found evidence that women engage less in bribery (Tuliao & Chen, 2017).…”

“…Moreover, research could be enriched by applying more individual variables, including gender. Personality traits can affect both silence and the creation and effective use of social networks and therefore social capital (Bon et al, 2017, Kilduff & Krackhardt, 2008Tuliao & Chen, 2017).Finally, cultural values can also impact the propensity to remain silent (Chelminski & Coulter, 2007;Sampaio, 2015), affecting ethical discourse and whistleblowing. Whistleblowing can be seen as inappropriate in countries with high levels of collectivism, power distance and uncertainty avoidance (Hofstede, 1980), and therefore, corruption prevention can be better achieved if emphasized as a violation of group-based ideals for behavior.…”

“…As Greve, Palmer and Pozner (2010, p. 68) discussed "many cases of organizational misconduct are collective efforts intentionally orchestrated by a number of interconnected individuals". Indeed, certain scholars have called attention to understanding which social network might be most likely to promote corruption and fraud (Bon, Volkema, & Silva, 2017;Brass, Butterfield, & Skaags, 1998;Nielsen, 2003;Huang & Rice, 2012). Corrupt individuals and/or organizations can develop alliance networks, using their social capital (power, influence, information) to "outsource" bribes (Spencer & Gomez, 2011) and create cartels (Bertand & Lumineau, 2016).…”

“…Moreover, cultural aspects were related to corruption such as the Brazilian jeitinho (Torres, Alfinito, Galvão, Tse, 2015) or can explain avoidance to whistleblowing (Behrens, 2015). However, very few studies investigated corruption using the network lens to identify corrupt actors (Bon, Volkema, Silva, 2017;Ribeiro, Alves, Martins and Leniz, 2018) or personal relationships and individual factors which can impact whistleblowing (Sampaio, 2015;Santos, Guevara &Amorim, 2013).…”