Abstract

As innovative technologies spread, the role of means of interaction with customers in the digital environment is growing. Virtual reality technologies are becoming one of the most promising tools for significantly expanding customer interaction in the context of digital transformation. The purpose of this article is to analyze the brand strategies of the leading companies and show the possibilities of using virtual reality technology and the concept of the metaverse for brand formation and promotion. We also aim to assess the prospects and formulate recommendations on the use of the digital environment in brand management strategies. To achieve the set goal, several tasks were completed. The authors analyzed the experience of using the digital environment for interacting with customers and studied the influence of VR, AR, MR, and XR technologies on brand management. Additionally, the possibilities of using the metaverse concept for brand management was defined. In this work, general scientific theoretical and empirical research methods were applied. The study furthers the theoretical foundations of brand management in the digital environment via innovative branding technologies that support an increase in the competitive performance of companies.

Introduction

As information and communication technologies develop, the digital environment forces enterprises to rethink their strategic goals, transforming both the business itself and the methods of doing business. The use of digital technologies in marketing offers completely new value propositions and ways to promote the business [1].

Traditionally, promotion (communicative mix), as an element of marketing mix, combines various types of marketing communications, including advertising, direct marketing, public relations, etc. They are used to inform the target audience about the merits of a product, service, brand, or idea. Promotion is aimed at generating interest, raising awareness, increasing sales, or building brand loyalty [2]. The development of digital technologies has changed almost all the classic communication instruments and ways of obtaining information. Social networks, instant messengers, video hosting (e.g., YouTube, TikTok), and video conferencing tools (e.g., Zoom, Discord) are becoming more popular. Print media, TV, and radio channels are going digital and introducing online versions.

Conclusions

The analysis of the brand strategies of the leading companies showed quite ample opportunities for using the technologies of virtual, augmented, and mixed reality and the concept of the metaverse for the formation and promotion of brands.

New technologies and platforms, growing connections between communication channels, force companies to constantly seek and summarize best practices, working with customers and other stakeholders through various communication systems.

Branding in a virtual environment can often be challenging, but the benefits far outweigh the drawbacks. Therefore, virtual branding opportunities should be used to the maximum to gain competitive advantages.

1- Introduction

2- Method

3- Results

4- Discussion

References

Abstract

Intrasexual competition supports the acquisition of new mates and the maintenance of existing relationships. We investigated intrasexual competition in relation to Dark Triad traits (narcissism, Machiavellianism, and psychopathy) and intensity of Facebook use. Men (n = 61) and women (n = 164) completed measures assessing intrasexual competition (Buunk & Fisher, 2009), Dark Triad traits (Jones & Paulhus, 2014), and intensity of Facebook use (Ellison, Steinfield, & Lampe, 2007). Machiavellianism and psychopathy were positive predictors for endorsement of intrasexual competition. Narcissism and Facebook intensity did not predict intrasexual competition. Findings are discussed in relation to personality and mate competition.

Introduction

Intrasexual competition is a common sexual strategy, which functions to increase access to new partners and supports the maintenance of existing relationships. In humans, there is considerable within-sex variation in how competitive individuals are and the way in which people engage in competition. For example, gossip through social networks (Davis, Dufort, Desrochers, Vaillancourt, & Arnocky, 2017), or in online social media (Hendrickse, Arpan, Clayton, & Ridgway, 2017) are strategies that can be useful in intrasexual competition. Personality is another one important factor associated with competitive behaviour (Buunk, Bucksath, & Cordero, 2017; Goncalves & Campbell, 2014). In this brief report, we investigated intrasexual competition in relation to the Dark Triad personality traits (i.e., narcissism, Machiavellianism, and psychopathy) and intensity of Facebook use. The Dark Triad (Paulhus & Williams, 2002) is a widely researched personality constellation. The traits share a common core of manipulation and callousness (Jones & Figueredo, 2013) and influence a range of relationship types (Lyons, 2019). In particular, individuals at the higher end of the continuum have increased motivations for sexual partners (Lee et al., 2013), which appear to impact intrasexual competition (Carter, Montanaro, Linney, & Campbell, 2015). Each trait has been associated with a unique style of same-sex competition. For example, psychopathy is related to the use of deception (e.g., trying to impress a same-sex competitor by greeting an attractive member of the opposite sex; Jonason, Lyons, Baughman, & Vernon, 2014), as well as employing gossiping tactics that are aimed at damaging the reputation of the rival (Goncalves & Campbell, 2014). Narcissism has been connected to trying to outshine a competitor, and Machiavellianism to rude derogation of the competitor (Goncalves & Campbell, 2014). It is possible that social networking sites such as Facebook are also used as a forum for intrasexual competition (Wyckoff, Buss, & Markman, 2018). Social comparison and self-promotion are important features of social networking sites, especially amongst those who are younger or more intense users of these sites (Lee, 2014; Ozimek & Bierhoff, 2016). Aggressive behaviour such as cyberbullying and relational aggression are also commonplace online (Abell & Brewer, 2014; Slonje & Smith, 2008). Previous research demonstrates that relationship status influences men’s behaviour on such sites (McAndrew & Jeong, 2012), and attractiveness appears to influence the responses of other users (Wang, Moon, Kwon, Evans, & Stefanone, 2010). It is therefore possible that exposure to online promotion, comparison, and aggression encourages intrasexual competition. It is also plausible that those with an elevated drive for competition spend more time using these sites. Previous research has established sex differences in the intensity of Facebook use, indicating that women use it more than men do (Mcandrew & Jeong, 2012). Yet, it is not clear whether the sexes use social media differently as a tool in intrasexual competition, which is something that we will explore in this research. Furthermore, men tend to score higher than women on the Dark Triad instruments (Paulhus & Williams, 2002). However, rather than the sex of the individual, it is possible that the Dark Triad traits are more influential in intrasexual competition (Jonason et al., 2014). Therefore, we predict that irrespective of the sex of the participant, those high in the Dark Triad traits display greater levels of intrasexual competition.

Integer programming models for community detection in relational networks have diverse applications in different fields. From making our lives easier by improving search engine optimization to saving our lives by aiding in threat detection and disaster management, researches in this niche have added value to human experience and knowledge. Besides the community structure, the influential nodes or members in a complex network are highly effective at diffusing information quickly to others in the community. Prior research dealing with the use of optimization models for clustering networks has independently focused on detecting communities. In this research, we propose a new integer linear programming model to detect community structure in real-life networks and also identify the most influential node within each community. We validate the proposed model by testing it on a well-established community network. Further, the performance of the proposed model are evaluated by comparing it with the existing best performing optimization model as well as three heuristic approaches for community detection. The experimental results indicate that in most cases the proposed integer programming model performs better than the existing optimization model with respect to modularity, Silhouette coefficient and computational time. Besides, our model yields superior Silhouette and competitive modularity values compared to the heuristic approaches in many cases.

Introduction

A large number of complex systems can be expressed through networks, which is a group of nodes associated through edges or links (Raghavan et al., 2007). Social networks such as Facebook and Twitter are such examples, where the users are represented by the nodes and the ties/friendship between them is indicated by edges. Similarly, in supply chain logistics, nodes represent the supplier and customer locations, while edges denote the paths connecting the nodes. Due to its theoretical importance and practical applications, research on social, technological, physical and biological networks has gained importance in different branches of sciences. Representing the complex systems through relational networks provides an insight into the intricacy and the dynamic behavior of such systems (Pan et al., 2014). A network is said to exhibit a community structure if its nodes can be grouped into different clusters such that the nodes within the clusters are densely connected compared to the nodes outside the cluster (Radicchi et al., 2004). Finding communities in relational networks takes us one step further to better understanding complex systems, which in turn results in numerous benefits and applications (Girvan and Newman, 2002). Communities are formed by clustering the nodes in a relational network, where the nodes (or objects) are placed into groups such that the objects in the same group have maximum similarities, while the ones in different groups have the least in common.