Influence of Enterprise Social Networks on job performance - The role of attitude moderated effect

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Influence of Enterprise Social Networks on job performance

Marcos Henrique Marques Caetano Ramalho The role of attitude moderated effect

Thesis presented as partial requirement for obtaining the Master’s degree in Information Management with

specialization in Knowledge Management and Business

Intelligence

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Influence of Enterprise Social Networks on job performance The role of attitude moderated effect

m20190163

Marcos Ramalho

MEGI

2022

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ii NOVA Information Management School

Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa

INFLUENCE OF ENTERPRISE SOCIAL NETWOKS ON JOB PERFORMANCE – THE ROLE ATTITUDE MODERATED EFFECT

by

Marcos Ramalho

Thesis presented as partial requirement for obtaining the Master’s degree in Information Management with Specialization in Knowledge Management and Business Intelligence

Supervisor: Carlos Tam Chuem Vai, PhD

October 2022

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ACKNOWLEDGEMENTS

First and foremost, I would like to dedicate this thesis to the mentor of this scientific piece, who is Professor Carlos Tam. Carlos was an extraordinary mentor, having extraordinary patience considering the demands of my professional life, which have been very great during the last years.

I would also like to thank my “partner in crime”, Maria Rita Frade, for her patience and endurance during the time I was writing this thesis. Without her support and encouragement, it would be difficult to conclude this academic milestone.

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" Influence of Enterprise Social Networks on job performance - The role of attitude moderated effect

" submitted to a journal of quartile one of Scimago index

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ABSTRACT

The Covid-19 pandemic has affected our lives, increasing the pace at which Enterprise Social Networks (ESN) are being adopted and used. Recently, collaborative activities have increased sharply by 50%, since today 80% of professional people’s time is spent in meetings or answering colleagues. Combining the notion that collaborative work is becoming more and more important with the fact that by using social technologies companies can raise the productivity of “knowledge workers” by 20 to 25%, an important field of study has arisen. The current contribution is to go beyond the initial glimpse that Mcafee gave us about the topic in 2009. The objective is to investigate the extent to which ESN can explain productivity, and what the role of attitude moderated effect is on adoption/utilization and productivity over ESN. The study is focused on the individual level, considering an adaptation of the TTF model in which the attitude moderated effect is added. 215 responses were collected that explain 56.6% of the dependent variable, which is job performance. Considering the attitude moderated effect, it is notable that attitude, utilization, and TTF have an enormous impact on job performance.

KEYWORDS

Enterprise Social Network, enterprise 2.0, collaborative tools, job performance, productivity, attitude moderated effect

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TABLE OF CONTENTS

1. Introduction ... 1

2. Literature review & Hyphotheses... 3

2.1. Enterprise Social Networks ... 3

2.2. Task Technology Fit (TTF) & Hypotheses ... 4

3. Methodology ... 7

3.1. Measurement process ... 7

3.2. Data overview ... 7

4. Results... 9

4.1. Assessment of Measurement Model ... 9

4.2. Structural Model assessment ... 9

5. Discussion ... 11

5.1. Theoretical implications ... 11

5.2. Practical implications ... 12

5.2.1. ESN adaptation to tasks ... 12

5.2.2. ESN adoption by attitude ... 13

6. Conclusions ... 14

7. Limitations and recommendations for future works ... 15

8. Bibliography ... 16

9. Appendices ... 19

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LIST OF FIGURES

Figure 1 – Research model (Dishaw & Strong, 1999; Goodhue & Thompson, 1995) ... 6 Figure 2 – Research model results ... 10 Figure 3 – Attitude moderated effect ... 12

LIST OF TABLES

Table 1 - Audience main characteristics ... 8

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LIST OF ABBREVIATIONS AND ACRONYMS

CA Cronbach’s Alpha CR Composite Reliability ESM Enterprise Social Media ESS Enterprise Social System ESN Enterprise Social Network IS Information Systems MGI McKinsey Global Institute MOOC Massive Open Online Course TTF Task Technology Fit

TAM Technology Adoption Model

PLS-SEM Partial Least Squares, Structural Equation Modelling SEM Structural Equation Modelling

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1

1. INTRODUCTION

Over the years, terms like web 2.0, platform, enterprise 2.0, and Enterprise Social Networks (ESN) have invaded our daily vocabulary (Mcafee, 2009). However, and being more specific, there has been increasing attention to and investment in ESN (Bridgwater, 2019; Wehner et al., 2017). Besides the benefits that tools like Slack, Microsoft Teams, Cisco Webex, or Skype bring, namely an increase on productivity, collaboration, and non-routine tasks, collaborative overload has been continuously reported along with failures in the adoption of these kinds of tools (Cross et al., 2016; Wehner et al., 2017). To give some order of magnitude, 80% of people’s work time is spent in meetings or answering colleagues (Cross et al., 2016). At the same time, and according to McKinsey Global Institute (MGI), ESNs can increase the productivity of “knowledge workers”

by 20 to 25% (Chui et al., 2012).

However, and considering Cross et al. (2016), 20-35% of value-added collaborations come from only 3-5% of employees within an organization (Cross et al., 2016). Considering the Covid-19 pandemic and the fact that we are all more isolated from each other, it is fundamental to take the maximum from the aforementioned employees using ESNs (Kramer & Kramer, 2020).

Mcafee (2009) was the first academic to call for the importance of Enterprise 2.0 platforms.

Later, Leonardi et al. (2013) detailed the definition of ESN, which the current author employs for the study of this subject . Since Mcafee (2009) identified the so-called Enterprise 2.0 (that web 2.0 applied to companies is a field of knowledge that deserves deeper study), a great deal has been happening in the field. However, and besides the advances that have been experienced in the field, little academic literature has been produced about this topic. As such, this study is a contribution to this field, which is certainly going to capture academic attention in the years to come.

The aim of this work is to adopt a task technology fit (TTF) model, in which the fit among the task and the technology and utilization is fundamental to achieve job performance (Goodhue &

Thompson, 1995; Leonardi et al., 2013). TTF presumes that there should be a fit among the task and the technology that is used in conjunction with utilization to explain job performance (Goodhue & Thompson, 1995). When Goodhue and Thompson made their contribution, in 1995, it represented a true revolution in the way IS were evaluated, and was a milestone in IS research (Goodhue & Thompson, 1995). However, the model considers that a fit between technology and the task – utilization – is needed to explain job performance. Both TTF and utilization are independent variables considering the model, since a high TTF does not assume a high utilization or vice versa (Goodhue & Thompson, 1995). To improve TTF, attitude moderated effect was added to the model, since technology (especially at the beginning) could be used without an impact on job performance. To be more specific, while TTF focuses on external factors (the task, technology, and utilization factors), attitude introduces a variable that is related to the person that is the subject of study (Hu et al., 1999). With this model it is believed that job performance can be better explained by the utilization of ESN.

Furthermore, according to several studies, job performance explanation can vary widely (Chung et al., 2015; Tam & Oliveira, 2016). Job performance explanation normally varies from 0.4 to 0.6, depending on the formulated hypothesis and the model used (Goodhue & Thompson, 1995;

2 Klopping & McKinney, 2004). By a constructed model and formulated hypothesis, this document pretends to explain job performance considering 215 answers to a conducted survey.

This work brings two major contributions related to the role of ESN over individual job performance. First, there are no previous academic studies related to the impact of ESNs on individual job performance( Giermindl et al., 2018). This issue is especially important considering the Covid-19 pandemic, since people are now relying more on ESNs to ensure performance involving remote work. Second, there is no other research model in the literature that combines attitude moderated effect with TTF to study individual performance. The model in the present work aggregates the benefits of both task and technology performance with the individual characteristics of the ones that are performing the role (Chung et al., 2015; Dishaw & Strong, 1999; Goodhue & Thompson, 1995; Klopping & McKinney, 2004).

The structure of the paper is as follows. The ESNS literature review is examined, and the research model constructed considering individual performance dependent variable (job performance).

The methodology is then presented from data collection to hypothesis construction. Finally, results are presented and discussed, and suggestions for possible future research directions.

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2. LITERATURE REVIEW & HYPHOTHESES

2.1. E

S

N

There are several definitions in academia related to ESNs. However, there is one definition that was published by Leonardi et al. (2013) that is widely accepted amongst academics and professionals. ESNs are “web-based platforms that allow workers to (1) communicate messages with specific co-workers or broadcast messages to everyone in the organization; (2) explicitly indicate or implicitly reveal particular co-workers as communication partners; (3) post, edit, and sort text and files linked to themselves or others; (4) view the messages, connections, text, and files communicated, posted, edited and sorted by anyone else in the organization at any time of their choosing” (Leonardi et al., 2013). In this sense, and considering the same author, tools like blogs and wikis could not be considered as ESNs since these systems do not consider the four dimensions indicated (Leonardi et al., 2013). As such, enterprise wikis and blogs are considered to reside within the Enterprise 2.0 universe, but not within the ESNs universe specifically (Mcafee, 2009).

The implementation of ESNs in companies followed three paths (Leonardi et al., 2013):

(1) Use of available sites such as Facebook, Google+, or Twitter. DiMicco and Millen (2007) reported that many employees in several companies were using public sites to get to know their colleagues better or share relevant information. In fact, the use of public sites entails serious problems including the possible leakage of privileged information and/or hierarchical problems that could arise when managers have access to what their employees are writing to the public about the company (DiMicco & Millen, 2007). Facebook developed a business version in which information can be compartmentalized and filtered by topic or any other issue within the company. Taking this platform approach, some of the information leakage problems could be tackled, maintaining the same design that people were already used to.

(2) Private implementations of open source softwares. Another solution was the implementation of private systems, implemented on the company’s servers. According to Majchrzak et al. (2006), these kinds of tailormade solutions enhance collaboration and knowledge reuse but have little or no effect on the creation of new business opportunities.

(3) In-house solutions often built as prototypes by software vendors. In fact, this kind of solution is taken mostly by software or hardware vendors, that take this “communication challenge” as an opportunity to understand, create, and develop a solution that can be used both internally or externally embedded in a product that can be sold to the public. The Beehive system (Oracle Beehive) launched in 2007, which obtained 30,000 users in that same year, is an excellent example of the opportunities that are available in these markets for some of these vendors.

According to Leonardi et al. (2013), ESNs are fundamental to cultivate two kinds of knowledge:

instrumental knowledge and metaknowledge. Instrumental knowledge, also known as explicit knowledge, is the documented knowledge on how to do something. Metaknowledge is the knowledge regarding “who knows what” (Leonardi et al., 2013).

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2.2. T

T

F

(TTF) & H

Task technology fit (TTF) focuses on the individual adoption of a certain technology. In fact, it highlights the importance of the fit of a certain technology to perform a certain task (Dishaw &

Strong, 1999; Goodhue & Thompson, 1995). The methodology is based on information about the tasks that are performed at an individual level, in which the information is contrasted with the technology that is used to perform the task. According to the model, the fit between technology and the task is linked to the use of the tool, such as performance itself. In fact, TTF assumes that the technology must be utilized, and there must be a good fit between the technology and the task that the technology proposes to address (Goodhue & Thompson, 1995).

The development of the research model focuses on three main ideas: 1) TTF - TTF explains job performance through both technology and the task that is being performed, and is considered to be one of the best models to explain this dependent variable (Goodhue & Thompson, 1995).

2) Attitude dimension - Despite the revolution that the TTF model introduced, the TTF model lacks the individual dimension of the person that is using the technology at the individual level (Dishaw & Strong, 1999). As a result, attitude was added to the model. 3) The dependent variable job performance - Job performance could be measured on two several levels, individual and organizational, and is a multi-dimensional and dynamic concept (DeLone & McLean, 2002;

Sonnentag & Frese, 2002; Tam & Oliveira, 2016). Job performance can have several perspectives (Sonnentag & Frese, 2002). Performance has long been the subject of study (Sonnentag & Frese, 2002; Tam & Oliveira, 2016). Organizations depend on both organizational and individual performance for their success. These concepts are intrinsically related since organizational performance results from the sum of the individual outcomes of their contributors, and is considered to be the effectiveness of an individual to achieve his/her predefined goals (Sonnentag & Frese, 2002). In our literature review we found no studies of the effect of ESN on the individual performance and their capacity to help an individual to achieve a certain goal.

Studying individual performance with the TTF model is an interesting and important undertaking (Goodhue & Thompson, 1995; Tam & Oliveira, 2016).

Task characteristics - A task is an action carried out that turns inputs into outputs (Goodhue &

Thompson, 1995). According to Goodhue and Thompson (1995), task characteristics have a high correlation with TTF and thus utilization and job performance, especially if non-routine tasks are considered (Goodhue & Thompson, 1995). Since the task characteristics play a central role, it is perceived that task characteristics could explain a large part of ESNs job performance, especially regarding non-routine tasks that are done through ESN systems (Klopping & McKinney, 2004).

H1 – Task characteristics influence TTF.

Technology characteristics - Technology characteristics are viewed as the tools used by an individual to perform the tasks (Goodhue & Thompson, 1995). According to Goodhue and Thompson (1995), no system provides perfect data to meet complex task needs without any expenditure of effort and technology. According to these authors, complex tasks are also more technologically demanding.

5 H2 – Technology characteristics influence TTF.

Task technology fit - Considering the literature about TTF, TTF recognizes both a task and a certain tool, whereby the TTF is “the degree to which a technology assist[s] an individual in performing his or her portfolio tasks” (Goodhue & Thompson, 1995). TTF also addresses the fit among tasks that are performed (Goodhue & Thompson, 1995; McGill & Klobas, 2009). TTF takes the rational approach that users use a certain technology because it improves the individual productivity (Dishaw & Strong, 1999). As TTF increases, it is assumed that there is a direct correlation on utilization (Zhou et al., 2010). If the perceived TTF is high, ESNs induce knowledge and information sharing, promoting the tool utilization (Lin & Huang, 2008). However, as mentioned above, TTF as a model neglects other motivations that could be subject to the use of a certain technology for a certain task (Dishaw & Strong, 1999). For this reason the attitude factor was added to the model. However, and in this context, TTF needs to measure the effectiveness of this system considering the definition provided by Leonardi et al. (2013), in which ESNs systems need to be evaluated by the effectiveness of these tools to promote (1) communication, (2) reveal co-workers, (3) post, edit, and sort files, and (4) view messages, texts, and files edited by others in the organization.

H3 – TTF influences job performance.

H4 – TTF influences utilization.

Utilization - Utilization is the behaviour of employing the technology in completing tasks (Dishaw

& Strong, 1999; Goodhue & Thompson, 1995; Wu & Chen, 2017). Considering, for example, the early stages of the relationship between a task, an individual, and technology, it is possible for a person to use a tool with poor TTF, since the individual believes that in the long term this

"investment" is going to pay off, and job performance is affected in the short term (Dishaw &

Strong, 1999). It is believed that the proposed TTF research model provides a better explanation for both job performance and utilization, since the moderated effect of attitude is expected to influence and better explain job performance, as we will see below (Dishaw & Strong, 1999).

H5 – Utilization influences job performance

Attitude - Since TTF was first introduced by Goodhue and Thompson (1995), the methodology has been widely used in several fields of study related to technology adoption, namely mobile banking , Zhou et al. (2010), MOOC, Wu & Chen (2017), and E-commerce adoption, Klopping &

McKinney (2004 ). Despite the virtue of the TTF model in the interpretation of job performance through technology, due to the fact that TTF focuses on both TTF and utilization dimensions (H3, H4, and H5) to explain job individual performance, the model neglects other dimensions that could help to better explain the dependent variable (Dishaw & Strong, 1999; Goodhue &

Thompson, 1995). The simplicity of TTF neglects the fact that a technology, especially at the beginning, could be used without making a large impact on performance (Dishaw & Strong, 1999). As such, dimensions linked to the individual must be considered, namely attitude (Dishaw

& Strong, 1999; Goodhue & Thompson, 1995). Since attitude, especially at the beginning, could influence job performance since it keeps the individual “on track” when job performance has not achieved its peak, this new variable that is used in the technology adoption model (TAM) was introduced (Dishaw & Strong, 1999). Attitude certainly improves the model, helping to

6 explain the dependent variable job performance. In this sense, attitude differs from utilization and TTF since it is related to the technology on an individual level. Also, attitude introduces behaviour into the model, which is a dimension that is properly addressed in TAM but not in TTF. Attitude moderates the influence of TTF on job performance, such as the influence of utilization on job performance. To formalize this moderated effect, two hypotheses are added to the model:

H6 – Attitude moderates the effect of TTF on job performance.

H7 – Attitude moderates the effect of utilization on job performance.

Figure 1 – Research model (Dishaw & Strong, 1999; Goodhue & Thompson, 1995)

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3. METHODOLOGY

3.1. M

Several authors were consulted to address all six model dimensions, namely Zhou et al., (2010) for task and technology characteristics, Lin and Huang (2008) for TTF, Wu and Chen (2017) for attitude, and Urbach et al. (2010) for utilization and job performance. All questions were adapted considering the ESN context, and the questionnaire was made in both English and Portuguese (see Error! Reference source not found. in Appendix for the questions). The questionnaire was distributed using digital means as is detailed below.

All questions were measured on a Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree) except for the individual qualitative questions and age, which are (1) Which ESN do you use in the enterprise context? (Microsoft teams, Slack, Cisco Webex, Other (the respondent indicates which ESN he/she uses the most)), (2) Age, (3) Size of the organization the individual works for (Independent worker, Between 100 and 999 employees, Between 1,000 and 4,999 employees, Between 5,000 and 9,999 employees, More than 10,000 employees) (4) Gender (Feminine, Masculine; Other) (5) Country.

3.2. D

The survey was distributed through WhatsApp and posted on LinkedIn and Facebook specific groups on 4 August 2021. Data for the survey were collected from 4 August 2021 to 14 October 2021. 215 complete answers were collected from people working in 14 countries. Most respondents worked in Portugal (197). 57% of the respondents were men, and 87% worked for a company (187 respondents). The average age is 36.7 years. Regarding which ESN is used, the vast majority (83%) reported that Microsoft teams is their “main” ESN, while 4% use Slack and 2% use Cisco Webex. Also, 11% reported that other ESNs are used, namely Google Meet, Zoom or Skype (according to our definition, some of these kinds of tools are not ESNs) (Leonardi et al., 2013).

8 Table 1 - Sample main characteristics

Distribution ( n = 215) Gender

Male 126 57%

Female 89 43%

Age Distribution Company Size

<25 7 3% Independent

worker 8 4%

25-29 25 12% < 100 33 15%

30-34 95 44% 100-999 39 18%

35-39 20 9% 1000-4999 52 24%

40-44 24 11% 5000-9999 36 17%

45-49 21 10% >=10000 47 22%

>=50 23 11%

Country of residence Main ESN system used

Portugal 197 92% Microsoft

Teams 178 83%

United Kingdom 3 1% Slack 9 4%

Spain 2 1% Cisco Webex 5 2%

Turkey 2 1% Others 23 11%

Germany 2 1%

Others 9 4%

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4. RESULTS

To treat responses, structural equation modelling (SEM) was used, which is a statistical method to test and estimate causal relationships using a mixture of statistical data and qualitative causal assumptions (Tam et al., 2020). After SEM, partial least squares (PLS) was applied, which is a common method used in information systems research to test models (Tam et al., 2020). To materialize the treatment, Smart PLS 3 software was used. Given PLS, the measurement model was analysed for both consistency and validity (convergent and discriminant). Finally, the model was tested for the hypotheses and attributes.

4.1. A

M

M

The table in Appendix B reveals that internal consistency is guaranteed, since both composite reliability (CR) and Cronbach’s alpha values are above the 0.7 threshold (Isaac et al., 2019). In fact, the minimum value on Composite Reliability (CR) is obtained on utilization, with a value of 0.862, and the minimum value on Cronbach’s Alpha (CA) is 0.795, obtained on technology characteristics. Considering the convergent validity, Appendix B indicates that the values for all variables comply with these criteria, since all values present an average variance extracted above 0.5. It can thus be inferred that each variable that was chosen explains more than half of the variance of their indicators. Finally, considering discriminant validity (Appendix D), the loading of each factor is well above the cross loadings. Also, the maximum value of the Heterotrait-Monotrait Ratio (HTMT) (Appendix C) is 0.691, which is below the threshold value of 0.9 (Henseler et al., 2015). Thus, discriminant validity has been established (Hair et al., 2012;

Isaac et al., 2019).

Having validated the measurement model (internal consistency, convergent validity, and discriminant validity), we check if the structural model is coherent with the practical model.

4.2. S

M

Structural models were tested through computations of beta (β) for a 10% significance, R², and respective t-values with use of a bootstrapping technique, according to a 5,000 resampling (J.

Hair et al., 2022).

As seen in Figure 2, all the hypotheses were confirmed except for the attitude moderated effect between TTF and job performance (H6). The model explains 56.6% of job performance variation, which is a good result, since the model explains more than half of the dependent variable. Task technology fit (𝛽̂ = 0.272; 𝑝 < .05) and utilization (𝛽̂ = 0.260; 𝑝 < .01) are statistically significant in explaining job performance, thereby supporting hypotheses H3 and H5. The model explains 38.1% of variation of utilization. Task technology fit (𝛽̂ = 0.617; 𝑝 < .01) is statistically significant in explaining utilization, and thus hypothesis H4 is supported. The model explains 33.9% of utilization variation. Task characteristics (𝛽̂ = 0.215; 𝑝 < .01) and technology characteristics (𝛽̂ = 0.468; 𝑝 < .01) are statistically significant in explaining utilization, and thus hypotheses H1 and H2 are supported.

10 Figure 2 – Research model results

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5. DISCUSSION

In the two decades terms like web 2.0, platform, enterprise 2.0, and ESN have been used sometimes without context (Mcafee, 2009). At the same time, terms like performance, collaborative overload, and multidisciplinary teams are part of our 21^st-century world (Chui et al., 2012; Cross et al., 2016; Mcafee, 2009; Wehner et al., 2017).

Seeing all these variables, as well as “entropy“, Leonardi et al. (2013) created a definition of ESN, having in mind four premises. Given Leonardi et al.'s (2013) definition, Goodhue and Thompson's (1995) TTF model with attitude moderated effect was used as a way to study ESNs on job performance regarding five dimensions (Goodhue & Thompson, 1995; Leonardi et al., 2013). After constructing our model and outlining our hypotheses, we conducted an online survey in both Portuguese and English to collect data to initiate the study. The measurement model was analysed with regard to internal consistency, convergent, and discriminant validity.

All hypotheses were confirmed except for hypothesis 6, “attitude moderates the effect of TTF on job performance”. The model explains 56.6% of job performance, which is considered a good result since more than half of the job performance variable is explained. Other studies investigating job performance through TTF regularly explain 40-60% of job performance (Chung et al., 2015; Goodhue & Thompson, 1995; Klopping & McKinney, 2004; Tam & Oliveira, 2016).

Given these results, job performance explanation determined with our model is broadly satisfactory and can add interesting findings to the academic community.

5.1. T

I

Three of our hypotheses have a direct impact on job performance, namely H3 - TTF influences job performance, H5 - utilization influences job performance, and H7 - attitude moderates the effect of utilization on job performance. When the values presented in Figure 2 (especially for H3 and H5) are compared to those of other published studies, the value of H5 is especially impressive since the impact of this variable is above other values seen in the literature (Chung et al., 2015; Tam & Oliveira, 2016). Furthermore, the results show that the moderated effect of attitude over utilization, such as the utilization on job performance, is statistically significant.

The use of ESN is suitable and tailored to the tasks that are being performed considering the information and work overload (Cross et al., 2016; Goodhue & Thompson, 1995; Mcafee, 2009).

Going into detail about attitude moderated effect on utilization and thus job performance, it can be seen in Figure 3 that attitude by itself could change the model explanation of job performance by almost 10%, especially if a high utilization is considered. Considering the slope of the two curves on the graph, users with high attitude play a significant role in job performance explanation, especially in a high-utilization scenario. This analysis shows how to empower utilizations, and most of all, job performance, i.e., by inducing a high attitude amongst the individuals that are using the technology. To be more specific, motivation plays a strong role in this issue, i.e., promote ESN utilization through motivation/attitude even if the results are not felt at the first glance.

12 Figure 3 – Attitude moderated effect

5.2. P

I

In this section, some practical implications are drawn considering the findings presented above, as it is critical to translate theoretical implications into practical ones.

5.2.1. ESN Adaptation to Tasks

ESNs need to be tailored to the tasks that are being performed by individuals in organizations, the so-called TTF (Chin et al., 2015; Giermindl et al., 2018). However, since ESNs are not integrated in many companies’ organizational systems and processes, it is difficult for employees to enter onto another platform simply to “socialize with colleagues” (Chin et al., 2015; Giermindl et al., 2018). As utilization is one of the main problems associated with ESNs’ implementation, people need to find purpose on the use of these systems, integrating their day-to-day tasks with these kinds of tools (Chin et al., 2015). Aside from technological advances, corporate culture is still based on e-mail (Chin et al., 2015). ESNs represent a major opportunity to change this paradox, allowing teams to increase dynamics and performance.

Furthermore, is crucial to integrate these kinds of systems in terms of log-ins, removing barriers that can block these systems’ use while maintaining safety, since this is one of the greatest concerns to both employers and employees (Chin et al., 2015; Giermindl et al., 2018). For ESNs’

success, integration with other systems is fundamental. The integration of ESN with other systems like CRMs and ERPs should be an area of deeper study.

13 5.2.2. ESN Adoption by Attitude

As suggested by the theory of network externalities, the value of technology increases with the number of users (Katz & Shapiro, 1986). It is therefore essential that organizations use ESN to promote both explicit and implicit knowledge, encouraging ties that are now revealed due to silos breaking (Chin et al., 2015; Mcafee, 2009). In fact, utilization is usually reported as the main problem when implementing/adopting ESNs (Chin et al., 2015; Giermindl et al., 2018).

Contributing to this fact is the a lack of an “ESN culture” that promotes communications with these systems with a top-down approach (Chin et al., 2015). Usually, senior leaders do not use ESNs, and there is thus no incentive for employers or employees to use ESNs, since leadership is achieved by example (Chin et al., 2015).

Another important notion is that people do not always know the purpose of ESNs. Due to the aforementioned reason, many people use ESNs for internal self-promotion (Chin et al., 2015).

Besides top management involvement, it is crucial to establish the rules and explanation behind the use of ESNs. Also, organizations include several generations and roles that could benefit from tutoring regarding the system’s use, and ESNs are not an exception.

Finally, and considering geographical, philosophical, or religious differences, especially the fact that in some authoritarian countries social networks are monitored (provoking distrust amongst the target people that could use these systems), ESNs need to be a safe place for people to post, edit, and talk, using all of the potential that these systems can bring (Chin et al., 2015; Hofstede, 2011). The bottom line is that “ESN culture” needs to be part of the organization’s way of work, sharing content by ESN and creating a virtuous circle of content and connection.

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6. CONCLUSIONS

This study contributes to the increasing attention that has been attributed to a new and under- exploited domain, which is ESNs. After an extensive literature review, the TTF model and attitude dimension were aggregated with the use of PLS-SEM to explain job performance in 215 survey responses. Through this model 56.6% of job performance was explained, in which attitude moderated the effect of utilization over job performance. However, the hypothesis of attitude moderated effect on TTF was not proved by empirical evidence.

Upon analysing the model, it is clear that attitude plays a central role in ESN adoption. To promote attitude and thus job performance, it is fundamental to adapt ESNs to the enterprise tasks being performed, namely through the integration of TTF with other corporate systems, removal of access barriers, and the promotion of an ESN culture that induces attitude and, in the end, utilization. ESNs represent an enormous opportunity to change corporate culture, establishing “a new way of doing things”. For that, top management involvement is crucial, as is the creation of a safe and knowledgeable environment in which people can trade ideas.

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7. LIMITATIONS AND RECOMMENDATIONS FOR FUTURE WORKS

Future research efforts are needed to address the limitations of this study. First, it would be valuable to study the impact of individual user’s characteristics dimensions over ESNs, since each and every one of us have a different idea and relationship with ESNs, as with communication itself (Chin et al., 2015; Giermindl et al., 2018; Mcafee, 2009). Dimensions such as gender, religion, or geography could be added to understand how cultural issues (among others) interfere with job performance through ESNs, namely through the attitude dimension. Having in mind this purpose, the survey base should be wider, considering a larger number of individuals in different geographies, backgrounds, and contexts.

Secondly, functionalities that ESNs need to incorporate to increase job performance should also be a focus of study. According to the available literature, reasons like poor integration and need for authenticity validity were defined as constraints on ESN usage in the corporate context (Chin et al., 2015; Giermindl et al., 2018; Mcafee, 2009). Deeper study should be conducted to understand and identify other factors or ESN functionalities that can boost ESN usage and performance.

Thirdly, after considering the functionalities and characteristics that intervene with job performance, it is important to find the contribution of each variable (or ESN type) to job performance through a well-established model. It would then be possible to identify and prioritize which ESN or functionalities intervene the most in job performance (Chin et al., 2015).

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8. BIBLIOGRAPHY

Bridgwater, A. (2019). Microsoft Teams Hits 13 Million Users To Tighten Grip On Slack. Forbes.

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19

9. APPENDICES

Appendix A – Survey questions (adapted by the author)

Variable Question Adapted from

Task characteristics

TAC1 – I need to manage my work anytime anywhere

TAC2 – I need to communicate with my colleagues anytime anywhere TAC3 – I need to access information in real time

(Zhou et al., 2010)

Technology characteristics

TEC1 – ESNs provide ubiquitous services TEC2 – ESNs provide real-time services TEC3 – ESNs provide secure services

(Zhou et al., 2010)

Task technology fit

TTF1 – The functionalities of ESNs are very adequate for my job tasks TTF2 – The functionalities of ESNs are very appropriate for my job tasks TTF3 –The functionalities of ESNs are very useful for my job tasks TTF4 –The functionalities of ESNs are very compatible with my job tasks TTF5 – The functionalities of ESNs are very helpful for my job tasks TTF6 –The functionalities of ESNs are sufficient for my job tasks TTF7 – The functionalities of ESNs make my job easier

TTF8 – In general, the functionalities of ESNs fit my job tasks

(Lin & Huang, 2008)

Utilization

U1 – I use ESNs to retrieve information U2 – I use ESNs to publish information

U3 – I use ESNs to communicate with colleagues U4 – I use ESNs to store and share documents

U5 – I use ESNs to retrieve colleagues’ contact information U6 – I use ESNs to retrieve competence profiles

U7 – I use ESNs to network with colleagues U8 – I use ESNs to work processes

(Urbach et al., 2010)

Attitude

ATU1 – Using ESNs technology is a good idea ATU2 – Using ESN is advisable

ATU3 – I am satisfied using ESNs

(Wu & Chen, 2017)

Job performance

JP1 – ESNs enable me to accomplish the tasks of my job quicker JP2 – ESNs improve my job performance

JP3 – ESNs increase my productivity JP4 – ESNS enhance my job effectiveness

JP5 – ESNs make it easier to accomplish my job tasks JP6 – ESNs are useful for my job

(Urbach et al., 2010)

20 Appendix B – Means, standard deviations, correlations, and reliability and validity measures

(CR, CA, and AVE) of latent variables

Constructs Mean Stdev CR CA TaskC TechC TTF Use JobPerf Task characteristics 5.800 1.341 .906 .846 .874

Technology characteristics 5.847 1.042 .880 .795 .360 .843 Task technology fit 5.623 1.097 .952 .942 .386 .546 .861

Utilization 5.363 1.126 .862 .808 .346 .441 .617 .715

Job performance 5.459 1.200 .960 .950 .450 .376 .660 .599 .895

Appendix C –Heterotrait-Monotrait Ratio (HTMT)

Constructs TaskC TechC TTF Use JobPerf

Task charateristics

Technology characteristics .429

Task technology fit .426 .624

Utilization .416 .536 .691

Job performance .503 .419 .690 .669

21 Appendix D – PLS loadings and cross-loading

Constructs TaskC TechC TTF Use JobPerf

Task characteristics TAC1 .883 .331 .355 .256 .383

TAC2 .880 .306 .280 .316 .394

TAC3 .858 .303 .362 .337 .401

Technology characteristics TEC1 .368 .847 .432 .410 .369

TEC2 .348 .919 .547 .428 .383

TEC3 .176 .755 .383 .261 .174

Task technology fit TTF1 .349 .462 .868 .492 .557

TTF2 .402 .508 .886 .467 .543

TTF3 .330 .479 .892 .616 .545

TTF4 .294 .503 .853 .487 .481

TTF5 .298 .449 .860 .586 .610

TTF7 .345 .433 .802 .551 .637

TTF8 .304 .459 .860 .502 .586

Utilization U1 .296 .378 .489 .796 .475

U2 .224 .277 .488 .764 .473

U3 .278 .402 .552 .696 .441

U4 .191 .329 .371 .732 .365

U5 .254 .285 .390 .666 .288

U8 .230 .198 .306 .623 .489

Job performance JP1 .460 .376 .642 .561 .875

JP2 .439 .318 .532 .512 .923

JP3 .394 .325 .555 .536 .912

JP4 .473 .317 .552 .486 .911

JP5 .345 .287 .590 .504 .897

JP6 .313 .381 .646 .595 .849