The behavior of young people at the beginning of their career through LinkedIn

O comportamento dos jovens em início de carreira por meio do LinkedIn

Abstract

Purpose:

The primary purpose of this article is to understand the behavior of young people at the beginning of their careers, in the context of Covid-19, considering the intention to continue using the services of the professional social network LinkedIn.

Originality/value:

This article expands the scope of studying a user group from acceptance in the continuance intention (continuous use) of LinkedIn, analyzing a series of positive and negative factors regarding the processes.

Design/methodology/approach:

We carried out a descriptive quantitative approach. The authors collected data from a survey (n = 386). To test the hypotheses, the researchers applied the technique of structural equation modeling (SEM), with estimation and partial least squares, and a moderating effect with the technological optimism construct for the predictive variables of the intention to continue using.

Findings:

The results indicated that all direct and indirect relationships were accepted, except a moderation (H7d) which was rejected, considering that users less optimistic about the technology may feel positively overwhelmed regarding the service’s perceived usefulness. LinkedIn’s variety of services can lead users to continue using the platform. The platform’s objective is to facilitate collective self-presentation, keeping profes sional relationships active. The results also indicated that the sample respondents are more likely to be actively and semi-passively involved on LinkedIn.

Keywords:
social media; professional social network; LinkedIn; career; undergraduate students

Resumo

Objetivo:

O objetivo principal deste artigo é compreender o comportamento de jovens em início de carreira no contexto da Covid-19, considerando a intenção de continuar utilizando os serviços da rede social profissional LinkedIn.

Originalidade/valor:

Este artigo amplia o escopo dos estudos de aceitação na intenção de continuar utilizando (uso contínuo) o LinkedIn, analisando uma série de fatores positivos e negativos em relação aos processos.

Design/metodologia/abordagem:

Realizou-se uma pesquisa descritiva com abordagem quantitativa. Os autores coletaram dados de um survey (n = 386) e, com o objetivo de testar as hipóteses, aplicaram a técnica de modelagem de equações estruturais (MEE), com estimação de mínimos quadrados parciais, e um efeito moderador com o construto otimismo tecnológico para as variáveis preditivas da intenção de continuar utilizando.

Resultados:

Os resultados indicaram que todas as relações diretas e indiretas foram aceitas, exceto uma moderação (H7d), que foi rejeitada, visto que os usuários menos otimistas com a tecnologia podem se sentir positivamente sobrecarregados quanto ao nível de utilidade percebida do serviço. A variedade de serviços do LinkedIn pode levar o usuário a uma intenção de continuidade no uso da plataforma, uma vez que o objetivo dessa rede é facilitar a autoapresentação coletiva, mantendo as relações profissionais ativas. Os resultados também indicaram que os entrevistados da amostra são mais propensos a se envolverem ativa e semipassivamente no LinkedIn.

Palavras-chave:
mídia social; rede social profissional; LinkedIn; carreira; estudantes de graduação

INTRODUCTION

The development of information and communication technologies (ICTs) has changed the job search process significantly, introducing new formats and reallocating efforts among diverse online platforms, including those within the professional social network model (Stevenson, 2009).

There are several professional social networks worldwide, such as LinkedIn, Bebee, Bayt, Xing, and Viadeo. However, LinkedIn is the most popular and influential professional social networking site (SNS) globally and the most used in Brazil, even where other online social networks, such as Facebook, can be used as a space to advertise job vacancies.

Microsoft acquired the LinkedIn platform in 2016, and, currently, it holds more than 675 million users monthly, of which 57% are men and 61% of users are between 25 and 34 years old (Lacoste, 2016; Roulin & Levashina, 2018; Begnini et al., 2019). There are around 30 million companies in 200 countries, in which their advertisements can reach 12% of the world population. Also, 57% of users access the platform via smart-phone. Moreover, studies were conducted on the relationship between gender and use of the website and users’ income levels (Blank & Lutz, 2017; Zide et al., 2014).

There are around 41 million active users in Brazil (Cooper, 2020). LinkedIn has expanded its resources in recent years and has become a robust and multifaceted social network, further differentiating itself from other resume hosting and job search sites (DeKay, 2009). Besides, the expansion of new analytical and publishing functions encourages members to use LinkedIn for business purposes and not just as a tool for searching for jobs (Forret, 2018).

Despite the increasing use of LinkedIn, few academic studies have examined the possible professional benefits obtained on these sites, indicating that it is not the number of contacts that an individual achieves on the network but the frequency of use that is important for receiving career benefits (Davis et al., 2020). Also, LinkedIn can be associated with more significant informational benefits, as well as activities carried out in groups of contacts, and the number of connections can affect this type of network in obtaining resources and self-promotion results in the career overtime (Basak & Calisir, 2014; Blank & Lutz, 2017).

Developing a network strategy is essential for the self-management of a career associated with success (Spurk et al., 2019). A professional social network can be the key to obtaining a job opportunity, especially for those who are more reclusive (Osburn, 2018).

Evidence-based research must be conducted to learn how users interact with LinkedIn as a professional social network and how this service can offer career benefits. This is especially important to understand with young people at the beginning of their careers since little is known about their behaviors and attitudes towards LinkedIn (Carmack & Heiss, 2018). Generally, this type of audience engages in less proactive search behaviors on LinkedIn due to their interest in creating profiles and observing who accesses them, rather than looking for others and making connections. On the other hand, there is a tendency for this audience to be using their network as a means of communication and actively developing personal profiles that include overly detailed curricula and histories, similar to other social networks (Gerard, 2012).

Additionally, the context of the Covid-19 pandemic poses even more significant challenges for young people’s entry and permanence in the labor market in Brazil. According to data published by IBGE ([Brazilian Institute of Geography and Statistics] 2021), the number of young people out of the labor market from 18 to 24 years old increased from 25.7%, in the third quarter of 2019, to 31.4% in the same period in 2020. This scenario made professional social networks more accessible due to searching for a job or partnership opportunity.

Therefore, professional SNSs can have relevant implications for the scale and content of career-related information exchange, increasing the importance of social networking in the labor markets (Brenner et al., 2020).

This article further explores how a user group may form the intention to continue using LinkedIn by analyzing positive and negative factors that influence trust (Chang et al., 2017), effort expectancy, social influence, privacy concerns, and perceived usefulness and perceived confirmation (Ashraf et al., 2020; Hsiao et al., 2016). The technological optimism construct was used as a moderating variable (technological optimism as a contingent factor) to verify whether it would affect the behavior among effort expectancy, social influence, trust, and perceived usefulness about the intention to continue using LinkedIn (Othman et al., 2020).

Thus, it is relevant to assess how professional social networks influence the labor market. The article’s objective was to understand the behavior of young people at the beginning of their careers, in the context of Covid-19, given the intention to continue using the services of the professional social network LinkedIn.

Therefore, this study seeks to contribute to the understanding of the use of the LinkedIn platform amid the health crisis – in which Brazil is one of the countries with the most worrying scenario (Andreoni, 2020). Add to this scenario the fragility of the economy, increased poverty, high unemployment rates, and low formal employment generation.

The following sections of this paper review the literature on digital platforms and LinkedIn, the leading research conducted in the area, and the existing gaps that this study intends to address. Next, we present the elaboration of the research hypothesis and research model. The description of the method and data collection supported analyzing the data and the results. Finally, the conclusions are presented, divided into theoretical and managerial implications, and the limitations and directions for future research.

LITERATURE REVIEW

Digital platforms and LinkedIn

Digital platforms are digital infrastructures that allow two or more groups to interact, bringing together several users, such as customers, advertisers, service providers, producers, suppliers, etc. There are several types of platforms with specific functions for several objectives. However, this article focuses on professional social networks, specifically the LinkedIn platform selected because it is a for-profit platform that has become the central infrastructure of some segments of social-economic life (Mackenzie, 2018).

A professional social network is a set of behaviors that aim to build and maintain interpersonal relationships that have the (potential) benefit of facilitating work-related activities, providing access to resources, and maximizing the members’ potential. When using social networks, accessing the platform will encompass a wide range of easily accessible candidates, specifically professionals focusing on recruitment from a company. This type of network, for example, LinkedIn, has a large number of registered active jobs. Therefore, professional social networks make the recruitment process possible at an increasingly lower cost (Khullar & Pandey, 2014).

Potential candidates can be classified into three different categories: active, semi-passive, and passive (Joos, 2008). Active candidates are those in the job search process; semi-liabilities are those interested in a new position but are not involved in finding a new job; liabilities refer to candidates who are currently employed and are not seeking a different opportunity in the market. Passive and semi-passive candidates would consider changing jobs if attracted by opportunities and conditions (Koch et al., 2018).

In general, candidates with desirable skills but more passive are presented by professional social networks to recruiters (Joos, 2008). Therefore, recruiters and organizations consider media and social networks attractive recruitment tools, allowing recruiters a competitive advantage in achieving their goals when done online (Singh & Sharma, 2014).

LinkedIn allows users to provide information about themselves and their contacts, enabling potential employers to search for job seekers’ information (Sameen & Cornelius, 2013). Although LinkedIn needs to follow guarantee principles and provide objective evaluations to candidates for a job vacancy, the expectation may vary according to each context, such as being recommended (Rui, 2018) or behaving appropriately on the platform. For example, sharing positive versus negative posts (Ziegele & Reinecke, 2017) or personal history becomes unacceptable for this network service, given its professional purpose (Bryant & Marmo, 2012).

RESEARCH HYPOTHESIS AND RESEARCH MODEL

For the elaboration of the research model (presented in Figure 1), we analyzed applications of models for accepting the use of technology to understand behavioral interactions and concentrated efforts on using the models of Chang et al. (2017), Hsiao et al. (2016), and Ashraf et al. (2020), according to the specificities of professional social networks. The study by Othman et al. (2020) was adapted, using the construct of technological optimism as a moderating variable.

Figure 1
Proposed research model

Effort expectancy (EE) and social influence (SI)

The Unified Theory of Acceptance and Use of Technology (Utaut) is a structure widely modeled in the study of the acceptance of new technologies and evaluating the probability of success of introducing new technologies (Venkatesh et al., 2003). The work of Sykes et al. (2009) was used to study the acceptance of technologies related to social networks. That theory integrates eight models of technology acceptance to explain the user’s intention to accept new technology and the behavior of subsequent use, containing, among others constructs, the effort expectancy and social influence. The preferred model was also adapted and expanded to study phenomena in services and products related to emerging technologies that influence user behavior (Kwon et al., 2014; Sykes et al., 2009) in several instances (Venkatesh et al., 2003). The Utaut model contributes to this study to better understand trust-related user behavior (Sameen & Cornelius, 2013). This article used previous studies to investigate the fundamental constructions, including the effort expectancy and social influence, given the phenomenon of influencers in social networks that have become market leaders who share their thoughts on several themes. This causes influencers to receive thousands of access views, affecting the intention to use and the actual behavior in terms of willingness to accept the continued use of professional social networks (Mackenzie, 2018).

Therefore, LinkedIn needs to offer its users fluent browsing, smooth interactions, and pleasant services to increase the user’s confidence. In this article, trust is defined as the user’s belief that professional social networks convey honesty and competence in protecting the user’s private information. Consequently, the hypotheses suggest the following: (Chang et al., 2017).

• H_1a: Effort expectancy positively affects trust in using LinkedIn.

• H_1b: Effort expectancy positively affects the continuance intention using LinkedIn.

Social influence is related to environmental determinants that affect behavioral intent and reflects people’s opinions on individuals affected by behavioral intention (Davis, 1989; Sykes et al., 2009). Also, it is proposed that friends, colleagues, and family influence the user’s trust in professional social networking services. Social influence is defined, in this study, as the degree to which users believe that their friends, colleagues, family members, and other relevant people, inside or outside LinkedIn, want them to continue to trust (Chang et al., 2017) in the use of the professional social network. Therefore, the following hypotheses are formulated:

• H_2a: Social influence positively affects trust in using LinkedIn.

• H_2b: Social influence positively affects the continuance intention using LinkedIn.

Privacy concerns (PR)

Protecting users against the improper retention of data and personal information causes privacy concerns since security breaches can cause unauthorized access, misuse, control, and appropriation of confidential or private data (Chang et al., 2015). In summary, privacy concerns reflect users’ concerns about disclosing personal information (Li, 2011). Users’ perceptions of professional social networks can also relate to protecting privacy, positively influencing trust in a platform (Shin, 2010). Regarding the previous information, this study is valuable to explore how privacy concerns affect user confidence in professional social networks. In this sense, this study considers privacy concerns and perceived risks as factors of negative impact on LinkedIn’s adoption (and continued use). Professional social networks require disclosing personal information on their website (Chang et al., 2015). Finally, several authors, including Lo (2010) and Wu et al. (2012), have presented models that bring the variable privacy concerns with adverse effects when related to trust. Due to this information, the following research hypothesis is proposed:

• H₃: Privacy concerns negatively affect trust in using LinkedIn.

Trust (TR)

Trust is defined as a willingness to accept a vulnerable situation based on a positive expectation of others’ actions. Some researchers use different influential factors to determine the tendency to trust and build trust as a central aspect in relationships (Riegelsberger et al., 2005). Trusting technology means that users are willing to depend on the system’s functions, as they believe that technology delivers the desirable attributes to protect their concerns. Trust is vital in virtual contexts and can be learned through social influence. Also, trust can be considered a positive predictor of people’s interactions and behaviors. From a human psychological perspective, trust has critical characteristics associated with risk (Jang et al., 2013). The user is likely to limit interaction with the service provider of a professional social network when there is a lack of trust (Chang et al., 2017).

The present study explored some of the main antecedents of trust related to effort expectancy, social influence, and privacy concerns. The trust constructs directly impact the user’s intentions and the continuous adoption of social networks (Chang et al., 2017; Zhou & Li, 2014) and can also be applied to professional social networks. Thus, users’ perception of trust in LinkedIn can positively affect the intention of continued use and perceived usefulness (Qin & Kong, 2015). In this study, trust is the user’s belief demonstrated by honesty, integrity, and competence to protect private information (Chang et al., 2017). Thus, the following hypotheses were formulated:

• H_4a: Trust positively affects the continuance intention using LinkedIn.

• H_4b: Trust positively affects the perceived usefulness of using LinkedIn.

Perceived confirmation (PC)

The perceived confirmation construct confirms initial expectations about productivity applications during use. The present study used the expectation-confirmation model (ECM) by Bhattacherjee (2001), formulated based on the theory that expectations and perceived performance influence the continuance intention of using social network services, both for personal and professional purposes.

Before using a service, the user develops expectations about it. After using it, the user has experiences and creates perceptions about its performance, and these results are the confirmation or disconfirmation of the expectations that existed before use. In turn, proof determines the level of trust and user satisfaction with the application service (Chen & Fu, 2018). Therefore, the degree of confirmation of user expectations and post-adoption expectations influence the form of perceived usefulness.

Studies have shown the model’s validity in many information technology products and services (Baker-Eveleth & Stone, 2015; Chen et al., 2011). The results revealed, among others, that the continuance intention was determined by the confirmation construct with a critical role, influencing usefulness and trust.

Given that the experience of using the service would confirm or not the expectation, users are led to update their post-adoption beliefs, corroborated by the technology acceptance models (Davis, 1989), and the level of confirmation that will also influence perceived usefulness.

In turn, the user’s level of trust regarding the professional social networking service is determined by this confirmation (Bhattacherjee, 2001; Hsu & Lin, 2016). Based on these insights, the following hypotheses are presented:

• H_5a: Perceived confirmation positively affects the perceived usefulness in using LinkedIn.

• H_5b: Perceived confirmation positively affects trust in using LinkedIn.

Perceived usefulness (PU)

Perceived usefulness results from evaluating the acquisition of a product or service. It is based more on a task-oriented perspective and less on an emotional view. The goal of technological innovation, in this case, the professional social networking service, is to increase performance and encourage efficiency. Therefore, utility systems provide instrumental and productivity-oriented value to social network users (Dogruel et al., 2015).

Based on a practical view, the model (Bhattacherjee, 2001) includes perceived usefulness as a variable that influences the intention to use, or even, in this case, the continuance of use (Davis, 1989). It is a degree of perception in which the individual believes that using a specific service or system improves performance. Consequently, we considered the following hypothesis:

• H₆: Perceived usefulness positively affects the continuance intention using LinkedIn.

Technological optimism (TO)

Technological readiness is the tendency of users to accept and use a new technology, usually assessed by their technical optimism levels regarding an individual’s positive belief about technology (Othman et al., 2020).

Technological optimism can be portrayed as a mental state resulting from mental inhibitors that collectively determine a person’s bias to use new knowledge. Many companies adopt and adapt technologies from other countries, as they trust foreign technologies, for example, to increase their customer base (Rojas-Méndez et al., 2017). Hallikainen and Laukkanen (2016) found a positive relationship between technological optimism and digital service acceptance. In the present study, technological optimism influences the relationship among the four constructs that predict the continuance intention using the LinkedIn platform. Based on the previous literature, the last hypothesis to be tested in this study is:

• H₇: Technological optimism moderates the effects of effort expectancy, social influence, trust, and perceived usefulness, positively affecting the continuance intention using LinkedIn. So that the higher the level of technological optimism, the stronger the relationship between the four constructs presented about the continuance intention using LinkedIn.

METHOD

This article presents a cross-sectional analysis of the participants obtained through a collection conducted with young people at the beginning of their LinkedIn users’ careers. We collected the sample from students of a business administration course at a private higher education institution in the city of São Paulo. The data were obtained for convenience through a survey conducted using QuestionPro online research platform. Of the 572 participants, only 424 completed the survey. After data purification using the Mahalanobis Distance (D2) criterion to identify outliers, there were 386 valid questionnaires for data analysis in IBM SPSS 25 and Smart PLS v. 3.3.3 Professional.

The survey included a sociodemographic section for the respondent’s profile and psychometric scales of the model proposed in this study. In the analysis phase of sociodemographic data, we sought to incorporate questions to cover aspects of the LinkedIn platform’s use. The data were treated using descriptive analyses between variables and means with independent samples (t-test).

The model was built with 39 questions anchored on a Likert type scale with seven points (1 – strongly disagree to 7 – totally agree), with 35 questions based on the adaptation of three studies (Ashraf et al., 2020; Chang et al., 2017; Hsiao et al., 2016) and for to the moderating variable (Othman et al., 2020).

The instrument was forward, back-translated, and validated by specialists in the field. We also used multivariate data analysis through Structural Equation Modeling, with partial least squares estimation (PLS-SEM). The moderating effect was analyzed using the Process v.3.4 macro (Hayes, 2017).

DATA ANALYSIS

Regarding the sample’s sociodemographic data, more than half of the respondents are female (61.9%/n = 239), and the average age was 23 years, with a high concentration of university students (96.9%/n = 374) and single people (85.8%/n = 331). Moreover, 70.5% (n = 272) are employed or work at some paid activity regarding the professional situation.

For 86% (n = 332) of the sample, the income is shared with other family members, and 72.5% (n = 280) live with a monthly payment of up to four minimum wages. It is worth mentioning that, for 48.4% (n = 187) of the total sample, Covid-19 affected the professional situation with the loss of jobs/internships, reduction of workload, home office work, return to the cities of origin, among other factors.

The use of job websites is recurrent among the sample. Regarding the specific use of LinkedIn, in the sample, the time of use of this professional social network is x = 12.59 months (just over one year). To those who use LinkedIn as the primary means of finding job opportunities (n = 98/25.4%), the frequency of use was “a few times a week”, with t₍₁₂₈₎ = 5.548 and p < .001. The vast majority, 72.4% (n = 71), represented by the individuals with family income range “less than four minimum wages”, pointed to the inclusive character of this professional social network (Chang et al., 2017). Only 2.59% (n = 10) of the respondents were successful and hired using LinkedIn. This result may be affected by the frequency of use, among other facts. According to Davis et al. (2020), this variable is essential for receiving career benefits, which may occur in the future, considering the sample profile.

The expansion of new analysis functions and publications on the LinkedIn platform has been expanded in recent years, thus bringing new priorities for use. However, the main focus is still on seeking a new job opportunity, 25.04%), and the perception of the advantages of having an account on this network website. This perception (34.97% of the sample) is aligned with the priority of use, reflected in the most significant recognized advantage, which is the possibility of being visible for the largest companies in the world (Basak & Calisir, 2014; Blank & Lutz, 2017; Forret, 2018).

Unlike in previous studies (Blank & Lutz, 2017; Zide et al., 2014), this research indicated homogeneity in all tests of analysis of the mean of the “sex” cluster variable when analyzing: version, time, and frequency of use, level of familiarity, primary means of looking for a job, participation in selection processes, success in getting a new job, and using LinkedIn as the primary device.

Analysis of the structural model

Considering that almost half of the sample suffered some impact at work during the Covid-19 pandemic, we carried out a multi-group of analysis via parametric t-test using the categorical variable (Group_1 and Group_2). To assess the differences in coefficients and paths between the models, we used the assumptions of Chin et al. (2003).

The general result pointed out no significant difference in the relationships among the constructs between the groups and neither in the paths. People who perceived an impact with Covid-19 (Group_1) in their professional activities showed coefficients of determination slightly below the general average. The results were considered adequate, as shown in Table 1. Therefore, we opted for a grouped analysis of the data (n = 386).

Table 1
Multigroup analysis in the comparison of the determination coefficients (people with and without Covid-19 influence at work)

Influence from Covid-19 (Group_1) (n=187)	No influence from Covid-19 (Group_2) (n=199)
R2CI_i = 73.7%	R2CI_ni = 782%
R2TR_i = 55.4%	R2TR_ni = 652%
R2PU_i = 532%	R2PU_ni = 65.4%

• Source: Output from SmartPLS.

Regarding the predictor variables related to the latent variable “continuance intention”, it was possible to accommodate multicollinearity in the model – all values of the variance inflation factors (VIFs) showed results below 5, the lowest being SI1 = 1.385, and the highest, PC1 = 4.993.

After the first interaction, the results of the factor loadings obtained by the variables were presented, and only the variable SI5 = .277 (“I feel that the use of LinkedIn is a status symbol”) was excluded due to its factor load lower than .5. This was due to this professional social network’s character serving the purpose of including those who, for example, are looking for a job opportunity. It is worth pointing out that users can use the service in both its free and paid versions (in this research, 99% of respondents use the free version of LinkedIn).

After the adjustment, we verified the convergent validity, demonstrating how the latent variable correlates with the items chosen to measure that variable. The discriminant validity that involved the correlation between the research model’s constructs was adequate (Table 2).

Table 2
Convergent and discriminant validity

Constructs	Items	Convergent			Discriminant
		CA	CR	AVE	1	2	3	4	5	6	7
1 – Trust	5	.886	.917	.689	.830
2 – Perceived confirmation	3	.946	.965	.903	.679	.950
3 – Effort expectancy	5	.833	.881	.598	.639	.598	.773
4 – Social influence	4	.836	.890	.671	.374	.391	.369	.819
5 – Continuance intention	4	.902	.932	.773	.714	.742	.633	.416	.879
6 – Privacy concerns	4	.937	.955	.841	−.297	−.117	−.071	.065	−.183	.917
7 – Perceived usefulness	5	.942	.955	.811	.695	.719	.610	.399	.843	−.155	.901

• Source: SmartPLS outputs.
• * CA > .7; CR > .7; AVE > .5
• The highlighted diagonal shows the square roots of the AVE by the Fornell-Larker criterion.

The analysis of the measurement model must precede one of the relationships between the constructs or latent variables. The next step was to examine the measurement model (Table 2) regarding Cronbach’s alpha (CA), composite reliability (CR), average variance extracted (AVE), determination coefficients (R²) (Hair et al., 2016).

The CA coefficients indicated high internal consistency of the scales used, and the CR (Hair et al., 2016) and AVEs (Chin, 1998) the existence of convergent validity. The R² value measures the model’s predictive accuracy, which demonstrated that, in this study, the model is accurate and predictive of relevance in all constructs (perceived usefulness = .596; trust = .602 and continuance intention = .754).

Analysis of direct relations

We observe that hypothesis H1a with the path “effort expectancy → trust” (β = .340; p < .001) and hypothesis H1b with the path “effort expectancy → continuance intention” (β = .113; p < .01) were both accepted. When using LinkedIn’s services, users do not consider it difficult to develop skills to take advantage of the various opportunities of this social network, as they consider that learning to use it is easy. Also, the users establish a relationship based on trust, as they recognize transparency in the services offered and, again, since it constitutes a network service that allows interaction between the different subjects present in the platform (Chang et al., 2017). Another relevant point is the variety of services offered beyond placing the curriculum in a showcase to achieve a job opportunity as a priority. Thus, the user will continue to show interest in using the platform (Lacoste, 2016).

Although all causal relationships are validated, it is possible to observe that the ones that involve the social influence construct deserve attention. It is the case of hypothesis H_2a with the path “social influence → trust” (β = .105; p < .01) and H_2b with the path “social influence → continuance intention” (β = .056; p < .05). Both paths indicate that the LinkedIn platform cannot be understood as a traditional social network. It holds characteristics that differentiate it from others, such as Facebook, Instagram, and Twitter.

Within the LinkedIn platform, users appreciate when they receive compliments from colleagues. On the other hand, there is a concern with inelegant conduct and its impact on the professional circle, filling out the curriculum, and verifying the information presented.

Some digital influencers or celebrities frequently post content from experiences in the corporate world that can be authored or sharing news and articles from other sources, or even bringing questions for reflection on their work (Lacoste, 2016). In this perspective, influencers become references on various subjects, including, among others, entrepreneurship, employment, and technological innovation, using the LinkedIn Pulse news channel to publish texts and videos to achieve user engagement. Specialized careers companies may agree that users must have an “active” profile, seeking engagement with their network of contacts. However, LinkedIn and other network platforms can bring unexpected consequences to the proposed objectives, such as turning against the network’s users. Therefore, the activities developed in this professional relationship platform cannot be reduced to what is shared, and not everything should be seen as appropriate on the network.

There is social pressure to get a job – “force the search for a job” – as is the case with internships, which are an indispensable requirement for completing a course. There are also situations in which the only purpose of getting a job is filling in the needs or survival (DeKay, 2009), a fact corroborated by the sample with the concentration in the family income range of up to four minimum wages. Because of this, people seek areas that suit themselves by interest or appreciation of work to achieve professional growth within the company.

The negative relationship established in H₃, with the path “privacy concerns → trust” (β = .232; p < .001) was confirmed as expected, as these are concerns inherent to the availability of information on the internet, even if it is on a website recognized by its users as safe (Chang et al., 2015). The concern is due to the uncertainty regarding the use of personal data by LinkedIn and or third parties, considering that it can affect, for example, the professional image of the user (Li, 2011).

Regarding H_4a with the path “trust → continuance intention” (β = .190 p < .001) and H_4b with the path “trust → perceived Usefulness” (β = .384; p < .001), both hypotheses were accepted, and that bring up the concern of users with maintaining the promise established in the service contract, that is, that the platform retains and expands the services considered beneficial by users and that led them to accept the terms of the contract. By recognizing the activities offered as useful, trust is conveyed in meeting expectations. If the main focus is still on employment, not finding an opportunity compromise on the process, thus impairing the retention of these users on the platform (Jang et al., 2013).

Hypothesis H_5a with the path “perceived confirmation → perceived usefulness” (β = .458; p < .001), and hypothesis H_5b with the path “perceived confirmation → trust” ((3 = .408; p < .001) were accepted, indicating that, after the experience of usage, users evaluated the services offered by LinkedIn and confirmed their expectations on the use of this professional social network, that is, they were able to identify that the services provided were as good as what they expected. When observing the number of respondents who indicated that LinkedIn is their primary means of looking for a job (n = 98/25.4%), we should consider that the sample profile is composed of young university students in early career (Bhattacherjee, 2001; Hsu & Lin, 2016).

Hypothesis H₆, on the other hand, with the path “perceived usefulness → continuance intention”, was the one that showed the most significant effect (β = .620; p < .001). Probably, this is due to the increase in unemployment in various sectors affected by the Covid-19 pandemic. Thus, LinkedIn’s professional social network becomes an excellent relocation tool and other opportunities identified in the descriptive analysis. This indicates that the construct “perceived usefulness” establishes perspectives on the LinkedIn platform. It aims to increase the performance and visibility of the curricula and the tasks and encourage efficiency so that the “active” professionals can be replaced. On the other hand, “semi-liabilities” and “liabilities”, which is the profile most found in the market, at least before the pandemic, can also be favored (Joos, 2008; Koch et al., 2018).

Another indication is that LinkedIn profiles allow direct access to career sites, replacing the curriculum in the traditional format. Compared with other social network services, separating one “persona” from the other is possible. That is, the behaviors that fit in social life have no place in the context of work. Besides, some users can behave more privately. In this scenario, they will hardly share their lives on a worldwide network, which can provoke a mistaken judgment about their professional experience and competitive potential (Osburn, 2018). Perhaps this question concerns many users who see themselves in a platform that gains followers daily. That has become so relevant – or even omnipresent – as the user could be invisible if the individual is not participating in this social circle.

Analysis of indirect relations

To analyze this study’s mediations, we used Smart PLS, which determines if the mediated relationships are significant and the level of significance. An essential aspect of the analysis of mediation paths is that it makes explicit the direct and indirect effects of causal variables on dependent variables. Complete mediation is found when the direct relationship between the dependent and the independent variable is not considered significant, but the mediated relation is. In this sense, partial mediation is found when both direct and mediated relationships are considered significant. The two mediations found in this model were partial. However, we verified that the mediator variable trust improves the result of the direct relationship between effort expectancy and continuance intention. Also, both direct and indirect relationships (including Trust as a mediator) indicated similar results on the social influence and continuance intention path.

Analysis of moderating effects

The hypotheses H_7a (F_(1.382) = 6.979; p = .008), H_7b (F_(1.382) = 6.383; p = .011) and H_7c (F_(1.382) = 4.751; p = .029) were accepted, and H_7d was rejected (F_(1.382) = 2.992; p = .085). The measurement variables for the analysis of the moderations were based on the respective means (x) of each construct. Moreover, the intergroup analysis of these variables was divided into two groups with categorical data (low and high) and separated by the aggregated average of each variable (x_EE = 4.87, x_IS = 3.89, x_CO = 5.22, x_UP = 5.44 e x_OT = 5.00) (regarding the independent and moderator variables). However, the dependent variable “continuance intention” showed continuous scale data (variable continues).

These results can be seen in figures 2, 3, and 4 that increase the influence of the moderating variable technological optimism. The perception of understanding the effort expectancy, social impact, and trust inherent to using the LinkedIn platform increases concerning continuance intention. The linear relationships were positive and significant. Higher levels of these independent variables will produce higher levels of continuance intention in users. Similarly, lower levels of effort expectancy, social influence, and trust will result in low levels of continuance intention. These findings indicate that the independent variables affect the continuance intention, and it is more robust when technological optimism is high.

Figure 2
Moderating effect of technological optimism on the relationship between effort expectancy and continuance intention (H_7a)

Figure 3
Moderating effect of technological optimism on the relationship between social influence and continuance intention (H_7b)

Figure 4
Moderating effect of technological optimism on the relationship between trust and continuance intention (H_7c)

Figure 5 indicates that as technological optimism increases the perception of understanding the perceived usefulness inherent in the LinkedIn platform, it does not significantly interfere with continuing. The increase in the continuance intention is minimal when users have higher levels of technological optimism compared to the services offered by LinkedIn because they are highly accessible and easily found. Therefore, it is possible to assume that users who are more optimistic about using technology are more critical concerning problems related to the accessibility of the LinkedIn service. This may be because they are more aware of the latest versions and expect the technology to meet their needs and priorities. Meanwhile, users less optimistic about the technology may feel positively overwhelmed about the service’s perceived usefulness. One possible explanation would be that these users are less “tech-savvy” than their optimistic “influencers”.

Figure 5
Moderating effect of technological optimism on the relationship between perceived usefulness and continuance intention (H_7d)

CONCLUSION

Theoretical implications of this research

This work aimed to understand young people’s behavior at the beginning of their careers, in the context of Covid-19, considering the con tinuance intention using the services of the professional social network LinkedIn. Young people have been greatly affected by the pandemic since their job opportunities and or internships drastically reduced, which makes it difficult, for example, to continue studies and invest in professional qualifications. In this scenario, these young people, often attending higher education or recently graduated, find themselves competing for an opportunity with other professionals with more experience or postgraduate courses. The reduction in job opportunities causes people to apply for any type of opportunity available.

The results indicated that all direct and indirect relations were accepted, and only a moderation (H_7d) was rejected, considering that users less optimistic about the technology may feel positively overwhelmed regarding the service’s perceived usefulness.

The hypothesis H₆ had the greatest effect (β = .620; p < .001). The professional social network LinkedIn aims, among others, to increase the visibility of the curricula and the tasks already carried out. Many users find themselves compelled to be present in this network service because they feel that they may remain in obscurity if they are not.

The hypothesis H_2b had the lowest effect (β = .056; p < .05), bringing evidence that the LinkedIn platform cannot be understood as a traditional social network. Within it, users feel good when they receive compliments from colleagues. On the other hand, there is a concern with inelegant behavior and its impact on the professional circle.

One of the main implications of the results obtained in this research is that professional social networks like LinkedIn bring, in a single tool, multiple services that can be adjusted to the user’s priorities at any time. In the professional context, these benefits may include providing resources that support individuals in their work, such as increased job performance, leading to a promotion, or moving to a more attractive position.

Besides, LinkedIn provides two account versions, paid and free. In this last option, users can present themselves as “ideal” job candidates and demonstrate their positive image to potential employers. It can lead the user to a continuance intention using the platform, given that one of the network’s objectives is to facilitate collective self-presentation, to maintain active professional relationships.

Managerial implications

The theoretical model proposed in this research contributed to understanding 75.4% of the continuance intention using LinkedIn by young audiences, facilitating the construction of a bridge that strengthens the bonds between those who advertise a job opportunity and those who yearn for one. Primarily that works in a context where social media and digital skills have become a demand in professional qualification.

Another aspect identified in the research is that LinkedIn contributes to more equal opportunities in the job market and expands social justice. This may be especially true in a pandemic scenario, such as Covid-19, in which many people are inmates, due to social isolation, to a greater or lesser extent. In this context, people who are working from a home office arrangement, unemployed, or with a reduced workload find in this platform a place to look for an opportunity without leaving home, which is a valuable resource, as it helps reduce the risk of contagion.

LinkedIn is looking for ways to change its corporate identity, becoming a more attractive social network. To this end, it has been incorporating new services that transcend more reserved or serious characteristics, fostering interaction with elements closer to other social networks, keeping its users longer using their services, which meets the profile of young users.

Limitations and directions of future directions

The results indicated that respondents in the sample are more likely to be less proactively involved on LinkedIn, with a study on what could be conducted to encourage more active use by the users. This study pointed to a general understanding of how this group absorbs and learns about the use of these work platforms.

Future research can examine the pedagogical practices and the effects of the interaction among different behavioral perspectives with the insertion of different scales. Many university students and recent graduates use LinkedIn as a web platform for professional connections and search for internships and jobs. Hopefully, this study serves as a first step to identify how this sample profile understands and uses LinkedIn. Therefore, there is an opportunity to know how this digital platform can professionally assist e individuals in their current and future endeavors.

REFERENCES

• Andreoni, M. (2020, August 10). Coronavirus in Brazil: What you need to know. The New York Times https://www.nytimes.com/article/brazil-coronavirus-cases.html
  » https://www.nytimes.com/article/brazil-coronavirus-cases.html
• Ashraf, M., Ahmad, J., Sharif, W. , Raza, A. A., Salman Shabbir, M., Abbas, M., & Thurasamy, R. (2020). The role of continuous trust in usage of online product recommendations. Online Information Review, 44(4), 745–766. https://doi.org/10.1108/OIR-05-2018-0156
  » https://doi.org/10.1108/OIR-05-2018-0156
• Baker-Eveleth, L., & Stone, R. W. (2015). Usability, expectation, confirmation, and continuance intentions to use electronic textbooks. Behaviour & Information Technology, 34(10), 992–1004. https://doi.org/10.1080/0144929X.2015.1039061
  » https://doi.org/10.1080/0144929X.2015.1039061
• Basak, E., & Calisir, F. (2014, July 2-4). Uses and gratifications of LinkedIn: An exploratory study [Paper presentation]. Proceedings of the World Congress on Engineering, London, UK.
• Begnini, S., Santos, S. S. S. dos., Sehnem, S., Carvalho, C. E., & Machado, H. P. V. (2019). Capitalismo consciente: Uma análise netnográfica em grupos de rede social LinkedIn. Cadernos EBAPE.BR, 17(2), 277–293.
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  » https://doi.org/10.2307/3250921
• Blank, G., & Lutz, C. (2017). Representativeness of social media in Great Britain: Investigating Facebook, LinkedIn, Twitter, Pinterest, Google+, and Instagram. American Behavioral Scientist, 61(7), 741–756. https://doi.org/10.1177/0002764217717559
  » https://doi.org/10.1177/0002764217717559
• Brenner, S., Aksin Sivrikaya, S., & Schwalbach, J. (2020). Who is on LinkedIn? Self-selection into professional online networks. Applied Economics, 52(1), 52–67. https://doi.org/10.1080/00036846.2019.1638497
  » https://doi.org/10.1080/00036846.2019.1638497
• Bryant, E. M., & Marmo, J. (2012). The rules of Facebook friendship: A two-stage examination of interaction rules in close, casual, and acquaintance friendships. Journal of Social and Personal Relationships, 29(8), 1013–1035. https://doi.org/10.1177/0265407512443616
  » https://doi.org/10.1177/0265407512443616
• Carmack, H. J., & Heiss, S. N. (2018). Using the Theory of Planned Behavior to predict college students’ intent to use LinkedIn for job searches and professional networking. Communication Studies, 69(2), 145–160. https:// doi.org/10.1080/10510974.2018.1424003
  » https:// doi.org/10.1080/10510974.2018.1424003
• Chang, S. E., Liu, A. Y., & Lin, S. (2015). Exploring privacy and trust for employee monitoring. Industrial Management & Data Systems, 115(1), 88–106. https://doi.org/10.1108/IMDS-07-2014-0197
  » https://doi.org/10.1108/IMDS-07-2014-0197
• Chang, S. E., Liu, A. Y. , & Shen, W. C. (2017). User trust in social networking services: A comparison of Facebook and LinkedIn. Computers in Human Behavior, 69, 207–217. https://doi.org/10.1016/j.chb.2016.12.013
  » https://doi.org/10.1016/j.chb.2016.12.013
• Chen, S.-C., Chen, H.-H., Lin, M.-T., & Chen, Y.-B. (2011). A conceptual model to understand the effects of perception on the continuance intention in facebook. Australian Journal of Business and Management Research, 1(8), 29–34.
• Chen, J. H., & Fu, J.-R. (2018). On the effects of perceived value in the mobile moment. Electronic Commerce Research and Applications, 27, 118–128. https://doi.org/10.1016/j.elerap.2017.12.009
  » https://doi.org/10.1016/j.elerap.2017.12.009
• Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publishers.
• Chin, W. W. , Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/ adoption study. Information Systems Research, 14(2), 189–217. https://doi.org/10.1287/isre.14.2.189.16018
  » https://doi.org/10.1287/isre.14.2.189.16018
• Cooper, P. (2020, March 9). 20 LinkedIn statistics that matter to marketers in 2020 Social Media Marketing & Management Dashboard. https://blog. hootsuite.com/linkedin-statistics-business/
  » https://blog. hootsuite.com/linkedin-statistics-business/
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  » https://doi.org/10.2307/249008
• Davis, J., Wolff, H.-G., Forret, M. L., & Sullivan, S. E. (2020). Networking via LinkedIn: An examination of usage and career benefits. Journal of Vocational Behavior, 118, 103396. https://doi.org/10.1016/j.jvb.2020.103396
  » https://doi.org/10.1016/j.jvb.2020.103396
• DeKay, S. (2009). Are business-oriented social networking web sites useful resources for locating passive jobseekers? Results of a recent study. Business Communication Quarterly, 72(1), 101–105. https://doi.org/10.1177/1080569908330378
  » https://doi.org/10.1177/1080569908330378
• Dogruel, L., Joeckel, S., & Bowman, N. D. (2015). The use and acceptance of new media entertainment technology by elderly users: Development of an expanded technology acceptance model. Behaviour & Information Technology, 34(11), 1052–1063. https://doi.org/10.1080/0144929X.2015.1077890
  » https://doi.org/10.1080/0144929X.2015.1077890
• Forret, M. L. (2018). Networking as a Job-Search Behavior and Career Management Strategy Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199764921.013.022
  » https://doi.org/10.1093/oxfordhb/9780199764921.013.022
• Gerard, J. G. (2012). Linking in with LinkedIn: Three exercises that enhance professional social networking and career building. Journal of Management Education, 36(6), 866–897. https://doi.org/10.1177/1052562911413464
  » https://doi.org/10.1177/1052562911413464
• Hair, J., Hult, G. T. , & Ringle, C. (2016). A primer on partial least squares structural equation modeling (2nd ed.). Sage Publications.
• Hallikainen, H., & Laukkanen, T. (2016). How technology readiness explains acceptance and satisfaction of digital services in B2B healthcare sector? [Paper presentation]. Pacific Asia Conference on Information Systems, Chiayi, Taiwan. https://aisel.aisnet.org/pacis2016/294
  » https://aisel.aisnet.org/pacis2016/294
• Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). The Guilford Press.
• Hsiao, C.-H., Chang, J.-J., & Tang, K.-Y. (2016). Exploring the influential factors in continuance usage of mobile social apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342–355. https://doi.org/10.1016/j.tele.2015.08.014
  » https://doi.org/10.1016/j.tele.2015.08.014
• Hsu, C.-L., & Lin, J. C.-C. (2016). Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention. Technological Forecasting and Social Change, 108, 42–53. https://doi.org/10.1016/j. techfore.2016.04.012
  » https://doi.org/10.1016/j. techfore.2016.04.012
• IBGE (2021, March 7). Pesquisa Nacional por Amostra de Domicílios Contínua Trimestral https://sidra.ibge.gov.br/tabela/4094.
  » https://sidra.ibge.gov.br/tabela/4094.
• Jang, Y.-T., Chang, S. E., & Chen, P.-A. (2013). Exploring social networking sites for facilitating multi-channel retailing. Multimedia Tools and Applications, 74, 159–178. https://doi.org/10.1007/s11042-013-1430-z
  » https://doi.org/10.1007/s11042-013-1430-z
• Joos, J. G. (2008). Social Media: New frontiers in hiring and recruiting. Employment Relations Today, 35(1), 51–59. https://doi.org/10.1002/ert. 20188
  » https://doi.org/10.1002/ert. 20188
• Khullar, A., & Pandey, P. (2014). Effective use of social media recruiting. International Journal of Intercultural Information Management, 4(4), 216–227. https://doi.org/10.1504/IJIIM.2014.067934
  » https://doi.org/10.1504/IJIIM.2014.067934
• Koch, T., Gerber, C., & De Klerk, J. J. (2018). The impact of social media on recruitment: Are you LinkedIn? SA Journal of Human Resource Management, 16 https://doi.org/10.4102/sajhrm.v16i0.861
  » https://doi.org/10.4102/sajhrm.v16i0.861
• Kwon, S. J., Park, E., & Kim, K. J. (2014). What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. The Social Science Journal, 51(4), 534–544. https://doi.org/10.1016/j.soscij.2014.04.005
  » https://doi.org/10.1016/j.soscij.2014.04.005
• Lacoste, S. (2016). Perspectives on social media ant its use by key account managers. Industrial Marketing Management, 54, 33–43. https://doi.org/10.1016/j.indmarman.2015.12.010
  » https://doi.org/10.1016/j.indmarman.2015.12.010
• Li, Y. (2011). Empirical studies on online information privacy concerns: Literature review and an integrative framework. Communications of the Association for Information Systems, 28 https://doi.org/10.17705/1CAIS.02828
  » https://doi.org/10.17705/1CAIS. 02828
• Lo, J. (2010). Privacy concern, locus of control, and salience in a trust-risk model of information disclosure on social networking sites AMCIS, Lima, Peru. https:// aisel.aisnet.org/amcis2010/110/
  » https://aisel.aisnet.org/amcis2010/110/
• Mackenzie, A. (2018). 48 million configurations and counting: Platform numbers and their capitalization. Journal of Cultural Economy, 11(1), 36–53. https://doi.org/10.1080/17530350.2017.1393443
  » https://doi.org/10.1080/17530350.2017.1393443
• Osburn, J. J. (2018, December 10). Five ways introverts can rule networking ClearanceJobs. https://news.clearancejobs.com/2018/12/10/five-ways-introverts-can-rule-networking/
  » https://news.clearancejobs.com/2018/12/10/five-ways-introverts-can-rule-networking/
• Othman, A. K., Hamzah, M. I., & Abu Hassan, L. F. (2020). Modeling the contingent role of technological optimism on customer satisfaction with self-service technologies: A case of cash-recycling ATMs. Journal of Enterprise Information Management, 33(3), 559–578. https://doi.org/10.1108/JEIM-09-2019-0295
  » https://doi.org/10.1108/JEIM-09-2019-0295
• Qin, L., & Kong, S. (2015). Perceived helpfulness, perceived trustworthiness, and their impact upon social commerce users’ intention to seek shopping recommendations. Journal of Internet Commerce, 14(4), 492–508. https://doi.org/10.1080/15332861.2015.1103634
  » https://doi.org/10.1080/15332861.2015.1103634
• Riegelsberger, J., Sasse, M. A., & McCarthy, J. D. (2005). The mechanics of trust: A framework for research and design. International Journal of Human-Computer Studies, 62(3), 381–422. https://doi.org/10.1016/j.ijhcs.2005.01.001
  » https://doi.org/10.1016/j.ijhcs.2005.01.001
• Rojas-Méndez, J. I., Parasuraman, A., & Papadopoulos, N. (2017). Demographics, attitudes, and technology readiness: A cross-cultural analysis and model validation. Marketing Intelligence and Planning, 35(1), 18–39. https://doi.org/10.1108/MIP-08-2015-0163
  » https://doi.org/10.1108/MIP-08-2015-0163
• Roulin, N., & Levashina, J. (2018). LinkedIn as a new selection method: Psychometric properties and assessment approach. Personnel Psychology, 72(2), 187-211. https://doi.org/10.1111/peps.12296
  » https://doi.org/10.1111/peps.12296
• Rui, J. R. (2018). Objective evaluation or collective self-presentation: What people expect of LinkedIn recommendations. Computers in Human Behavior, 89, 121–128. https://doi.org/10.1016/j.chb.2018.07.025
  » https://doi.org/10.1016/j.chb.2018.07.025
• Sameen, S., & Cornelius, S. (2013). Social networking sites and hiring: How social media profiles influence hiring decisions. Journal of Business Studies Quarterly, 7(1), 27–35.
• Shin, D.-H. (2010). The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with Computers, 22(5), 428–438. https://doi.org/10.1016/j.intcom.2010.05.001
  » https://doi.org/10.1016/j.intcom.2010.05.001
• Singh, K., & Sharma, S. (2014). Effective use of social media for talent acquisition and recruitment. International Journal of Intercultural Information Management, 4(4), 228–237. https://doi.org/10.1504/IJIIM.2014.067932
  » https://doi.org/10.1504/IJIIM.2014.067932
• Spurk, D., Hirschi, A., & Dries, N. (2019). Antecedents and outcomes of objective versus subjective career success: Competing perspectives and future directions. Journal of Management, 45(1), 35–69. https://doi.org/10.1177/0149206318786563
  » https://doi.org/ 10.1177/0149206318786563
• Stevenson, B. (2009). The internet and job search. NBER Chapters, 67–86. https://ideas.repec.org/h/nbr/nberch/3586.html
  » https://ideas.repec.org/h/nbr/nberch/3586.html
• Sykes, T. A., Venkatesh, V. , & Gosain, S. (2009). Model of acceptance with peer support: A social network perspective to understand employees’ system use. MIS Quarterly, 33(2), 371–393. https://doi.org/10.2307/20650296
  » https://doi.org/10.2307/20650296
• Venkatesh, V. , Morris, M. G, Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  » https://doi.org/10.2307/30036540
• Wu, K.-W., Huang, S. Y., Yen, D. C., & Popova, I. (2012). The effect of online privacy policy on consumer privacy concern and trust. Computers in Human Behavior, 28(3), 889–897. https://doi.org/10.1016/j.chb.2011.12.008
  » https://doi.org/10.1016/j.chb.2011.12.008
• Zhou, T., & Li, H. (2014). Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern. Computers in Human Behavior, 37, 283–289. https://doi.org/10.1016/j.chb.2014.05.008
  » https://doi.org/10.1016/j.chb. 2014.05.008
• Zide, J., Elman, B., & Shahani-Denning, C. (2014). LinkedIn and recruitment: How profiles differ across occupations. Employee Relations, 36(5), 583–604. https://doi.org/10.1108/ER-07-2013-0086
  » https://doi.org/10.1108/ER-07-2013-0086
• Ziegele, M., & Reinecke, L. (2017). No place for negative emotions? The effects of message valence, communication channel, and social distance on users’ willingness to respond to SNS status updates. Computers in Human Behavior, 75, 704–713. https://doi.org/10.1016/j.chb.2017.
  » https://doi.org/10.1016/j.chb.2017.

Publication Dates

• Publication in this collection
  03 June 2022
• Date of issue
  2022

History

• Received
  16 Mar 2021
• Accepted
  05 July 2021