The Effect of Social Media Usage on Staff Performance of Dr Hilla Limann Technical University

The Effect of Social Media Usage on Staff Performance of Dr Hilla Limann Technical University

Juliana Azaakandire, Zakaria Abdul-Rahaman

Dr. Hilla Limann Technical University

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000244

Received: 10 August 2025; Accepted: 17 August 2025; Published: 07 October 2025

ABSTRACT

This study investigated the effect of social media usage on performance of staff of Wa Polytechnic now Dr. Hilla Limann Technical University. The study adopted descriptive survey design, and gathered primary data from a sample of 98 respondents. Proportional stratified random sampling technique was used. Structured questionnaire was used to organize the data for the study. Data analysis was performed with inferential statistics in the form of OLS multiple regression and correlation analysis. The study discovered positive significant effects of social media usage and frequency of usage on workers’ performance. However, the effect of access to social media channels on employees’ performance was statistically insignificant. The study recommends that higher educational authorities should collaborate with telecommunication companies to make arrangements for the provision of free mobile data and relevant facilities that serve as the foundation for making social media accessible to all staff.

Keywords: Social Media, Job, Performance, Employee, Employer, Web-based, Mobile apps

INTRODUCTION

The role played by social media channels in human communications and interactions in general, and in the interactions of workers at the organizational setting, cannot be overemphasized. Interactions between people have changed significantly compared with what happened a few years ago as a result of the development and advancement of technology (O’Dell, 2011). Social media has become the basic means through which individuals get connected and engage in collaborative activities in and outside the workplace (Daowd, 2016). According to Shilu and Thriveni (2018), individuals’ daily activities and social media usage are inseparable, and it is difficult for persons to accomplish their activities without making use of some form of social media. The revolution and attractiveness of social media has been going solid for a long time now and Ghana has been no exception. The expansion in social media sphere could be ascribed to the rapid transformation of technology as computers have become more mobile, and the boom in mobile phone technology (Shilu & Thriveni, 2018), making people getting access to social media through their mobile phones on daily basis.

The use of social media in the workstation promotes workers’ misapplication of firm’s resources, official hours, and can cause deviant and non-compliant behavior of workers from prescribed operational procedures in the workplace. Thus, social media usage wanes and adversely accounts for employees’ work performance. The use of social media can pose threats and weakness to the organization when it adversely contributes to productivity, and when competitors get access to shared corporate information on social media platforms, it can become a threat.  Levy (2013) on the other hand; affirm that by incorporating social media usage in the business processes, employers and practitioners can improve efficiency and productivity of workers.

While some studies have been conducted in Ghana, prior research have concentrated purely on how social media usage accounts for academic performance of students (Boahene, Jiaming & Sarpong, 2019; Mingle, 2015; Owusu-Acheaw & Larson, 2015). Studies specially focusing on the effect of social media usage on employees’ performance in the context of higher educational institutions are virtually absent in Ghana. It is against these backgrounds that this study sought to investigate the effect of social media usage on the performance of workers of Wa Polytechnic now Dr. Hilla Limann Technical University. The focus on a tertiary education institution stems from the fact that in a learning environment like a Technical University, the use of social media for reasons of learning, information and knowledge exchange, and for the performance of official duties can be guaranteed. This study therefore focused on gathering data from both teaching (academic) and non-teaching (non-academic) staff of Wa Polytechnic now Dr. Hilla Limann Technical University to examine the effects of accessibility of social media tools, and the frequency of usage of such tools, as well as the purpose of utilization of social media on employees’ job performance at Wa Polytechnic now Dr. Hilla Limann Technical University.

Statement of the Problem

A study of the literature shows that prior studies on social media usage and employee job performance abound, albeit mixed results have been reported. Majority of prior research (Cetinkaya & Rashid, 2019; Cetinkaya & Rashid, 2018; Yulinda & Isana, 2017; Mahboud, 2018) found positive influence of social media usage on  workers’ work output, while some studies (Shilu & Thriveni, 2018) found negative impact. Both positive and negative effects have also been found by other studies (Hamed, 2017; Zulhamri & Taanya, 2019).

Despite the mixed results from studies across the globe, knowledge on the effect of social media usage on performance of workers in higher educational institutions in Ghana is virtually non-available because studies that investigate social media usage and employees performance are very limited in Ghana. Most of the studies conducted in Ghana rather focused on the effect of social media usage and academic performance of students (Boahene, Jiaming & Sarpong, 2019; Mingle, 2015; Owusu-Acheaw & Larson, 2015), failing to recognize that employees also use social media channels in their work premises, and that employers’ are also concern about productivity and employee performance on the job, require research attention.

This study aims at addressing these weaknesses in the literature in Ghana by examining the influence of social media usage on the performance of workers of Wa Polytechnic now Dr. Hilla Limann Technical University, focusing on the most common social media channels in Ghana such as Facebook, Twitter, YouTube, and WhatsApp.

Objectives of the Study

The general purpose of this study was to investigate the effects of social media usage on staff performance in Wa Polytechnic now Dr. Hilla Limann Technical University. The study focuses exclusively on achieving the following specific objectives.

1. To examine how access to social media channels in the workplace affect employees’ work performance
2. To assess the influence of frequency of social media usage on employees’ work performance at Wa Polytechnic now Dr. Hilla Limann Technical University
3. To ascertain the effect of social media usage on employees’ work performance at Wa Polytechnic now Dr. Hilla Limann Technical University.

Research Questions

The following research questions were formulated to guide the study.

1. How does access to social media channels in the workplace affect employees’ work performance?
2. What is the influence of the frequency of using social media on the work performance of employees’ at Wa Polytechnic now Dr. Hilla Limann Technical University?
3. What is the effect of social media usage on employees’ work performance at Wa Polytechnic now Dr. Hilla Limann Technical University?

LITERATURE REVIEW

The study presents a review of the existing literature with the view of providing information on the theories on which the study was situated and sheds light on what is already known to investigate the effects of social media usage on staff performance. It also discusses the concepts which form the foundations of the study. This paper was founded on social exchange theory, social cognitive theory, social network theory and technology acceptance model.

Social Exchange Theory

As all social media are based on information being created by the consumer, an awareness of the factors that people engage seems to be important. This hypothesis arose from studies in sociology that examined interaction between individuals or social classes (Emerson, 1976). The theory suggests that individuals participate in activities that are beneficial and keep away from activities that have too big a cost, that is, all social activity is focused on the skewed cost-benefit calculation of adding to a social interaction by each person. Individuals interact with each other due to the other negotiating group performing give-and – take acts (Emerson, 1976). This hypothesis is relevant to this study and that as workers use social media to connect, they get rewards and if not rewarded, they would quit.

Social Cognitive Theory

This is a psychological behavioural paradigm that originated mainly from the Bandura (1977, 1986) research. It has been established that it has significance in overcoming social behaviours. The principle of social cognitive tends to highlight that learning takes place in a social context, where much of what is taught is obtained by observation. The first theory is that psychological, behavioral and environmental factors influence one another in a bidirectional, give-and-take manner, that is, the ongoing functioning of an individual is a result of a non-stop interaction amongst cognitive, behavioral and contextual variables. The second premise is that in a definitive, goal-directed way, people have an organization to regulate their actions and the environment. The ideology disagrees with previous behavioral models which support a more rigorous type of environmental determinism.

The third theory is whether learning may proceed without immediate behavioural improvement or more generally whether learning and communicating what has been experienced are distinct procedures.

Social Network Theory

This theory defines interaction trends among humans as a graph of connections. Individuals within a network are called nodes, and participant connections are called ties. The ties and nodes make up the core of a social action network (Burt, 2001). The principle aims to explain the essence of a network and the network’s antecedents and implications at multiple rates that are hierarchical, inter-unit or inter-organizational. Studies showed that accelerated acquisition of information influences the flow of expertise and the efficiency of activities. Therefore, it will add to their success if citizens utilize the networks / connections to access information more easily.

Technology Acceptance Model (TAM)

The Technology Acceptance Model ( TAM) describes perceived usefulness in terms of social impact and cognitive instrumental mechanisms and use motives. Important theoretical and scientific evidence has accrued, in fact, in terms of the Technology Acceptance Model (TAM). Various observational experiments have shown that TAM reliably describes a significant proportion of the variation in consumption expectations and actions (typically around 40 per cent) and that TAM contrasts positively with alternative models.

Social media technology (SMT) has now become a “web-based and mobile apps that empower people and businesses to develop, engage and distribute new user-generated or current content in digital environments via multi-way communication.” Common smartphone and web-based social network channels include Facebook, Twitter, YouTube, Whatsapp , Instagram, Google Plus, snap chat etc. These channels have different tasks, responsibilities and contact styles while their features are largely linked to each other..

Conceptual Review

The paper looked at conceptual review of the concepts of social media and its various platforms and social media usage in Ghana. Other relevant concepts including job performance and its dimensions and factors that affect job performance were also discussed presented.

Social Media

Social media is a medium for ease of electronic contact, social networking, and teamwork (Carr & Hayes, 2015). Kaplan and Haenlein described social media as “a community of Internet-based applications which enable users to create and share user-generated content” (Kaplan & Haenlein, 2012). Social networking offers the opportunity to build social communication accounts that exchange details with search methods and privacy settings. As such, users can communicate a list of other users they connect with and connect with (Kane et al., 2014). Social media consists of three sections (Carr & Hayes, 2015) that are, machines generating and sharing material, machines gathering knowledge, and individuals accessing information for their official and private usage. These three sections enable its users to be connected to a single portal (Özdemir & Erdem, 2016; Varotto et al., 2016). Such networks enable the submission and exchanging of knowledge with no time and space limitations (Gao et al., 2018). Such platforms often provide the employees in a workforce with job-related knowledge which improves their efficiency and productivity.

Social Media Usage in Ghana

The IWS (2015) reported that figures on the world ‘s internet use are projected at 3,035,749,340 with a penetration rate of 42.3 percent as of June 2014. Similarly, Africa’s total population in 2014 was 1,125,721,038 of which 297,885,898 were internet users (IWS, 2015).  Although Ghana was the first Sub-Saharan African country to have access to the internet, internet penetration did not progress rapidly until 2005 (Quarshie & Ami-Narh, 2012). This could possibly be attributed to the Government’s ratification and adoption of Information and Communication Technology for Accelerated Development (ICT4AD) in the year 2004 (Quarshie & Ami-Narh, 2012). The International Telecommunication Union (ITU) statistics and IWS show that Ghana has seen a steady rise in the internet penetration rate. Table 5.1 reveals that there seems to be a correlation between population growth and internet usage.

Table 5.1: Population growth and internet penetration in Ghana

Source: Adopted from Internet World Stats, 2020; Quarshie & Ami-Narh (2012)

The increase in internet penetration could also be attributed to the rise of mobile-broadband subscriptions. As per the latest survey from the National Communications Authority (NCA) of Ghana ‘s telecommunications regulator, cell internet subscribers in the nation have grown steadily with a penetration rate of 59.78%. Mobile internet subscribers increased to 16,106,218 nationwide, as at the end of March 2015 (National Communication Authority, 2015). Statistics suggest that as more users connect to the internet and mobile phone, the faster the service connections increase (Mingle, 2015). Mobile subscription is required to enter the Website, and is used more easily to engage in social networking (Mingle, 2015).

The revolution of social media has been going solid for a long time now and Ghana has been no special case. The explosion in social media on the African scene could be ascribed to the mobile phone boom. In the third quarter of 2012, the 54 nations and 1.08 billion individuals have accumulated 821 million subscriptions, up by 16.9% year-on-year, bringing about a phone subscription penetration of 76.4%. Table 5.2 shows social media usage for mobile internet subscribers with the most utilized social media platform in Ghana being Facebook with the use of around 94.89%. Twitter positions second with 3.97%, Pinterest positions third with 0.62%, Google+ positions fourth with 0.18%. According to IWS (2019), the number of Facebook subscribers increased from 2,900,000 in 2015 to 4,900,000 in 2019. This shows that the utilization of Facebook for various reasons keeps on growing year after year in Ghana.

Table 5.2:  Social Media Usage in Ghana

Source: Adopted from Internet World Stats, 2020; Quarshie & Ami-Narh (2012)

Employee Job Performance- Definitions and Dimensions

In the foreseeable future, job success may be described as all sorts of work activities (Jex & M, 1998). Job success is correlated with employee capacity, knowledge of assigned targets, exceeding objectives and achieving delegated organizational goals (June & Mahmood, 2011; June & Mahmood, 2011). Job efficiency is the overall predicted benefit for an enterprise that an individual is working for a given time span. Linked to this, work performance is the efficiency of an individual anticipated in a particular job. Job success involves the work-related outcomes, principles, and accomplishments.

Various organisations have varying opinions on the use of social media in the workforce and the effect on job efficiency. Some are hopeful, for instance, and some are very worried about dropping efficiency (Cao et al . , 2016; White, 2014). However, other companies reap official advantages by introducing innovative communications technologies through constructive consumer and stakeholder participation (Odoom, et al., 2017).

Factors Affecting Employee Job Performance

Job efficiency applies to a staff’s level of job (Caillier, 2010). Job success is closely linked to employee productivity because the output of workers continues to improve due to an organizational stress management program (Haque, Aston & Kozlovski, 2018). Organizations who are conscious of this reality have focused completely on the variables impacting the job efficiency of the workers (Dinc, 2017). There are a variety of variables (internal and external) impacting job performances or a staff’s productivity within an enterprise. Internal factors include the individual’s ability, experience, knowledge and skills as well as the willingness to learn and adapt to organizational values and norms, while external factors are working environment, characteristics of assigned tasks, incentives, organizational structure and human resource management practices, including supervision and training and development opportunities (Lu, Gao, & Chen, 2015; Mericuz, 2015; Sani & Maharani, 2015).

Social Media Usage and Employee Job Performance

Overall, the usage of social media and the curiosity in it in the workforce has risen in recent years (Lovejoy & Saxton, 2012). Literature shows lack of consensus among scholars regarding the impact of social media usage on work performance. A large body of studies provides evidence of positive contribution of social media use in job performance of workers, while other papers argue in favour of the negative consequences social media use can have on job outcomes and organizational effectiveness. This section is keen to the empirical discussion of the arguments retrieved from the literature.

He examined the effects of organizational social media engagement on employee efficiency. They claimed that there are both positive and negative associations among employee performance and social media engagement. The hostile dynamic got deeper when staff spent more of their time expanding personal networks on Twitter. There are good partnerships through the usage of twitter in the workforce among workers for work-related as well as non-work-related events. Twitter can empower workers to establish partnerships and networks for information building and sharing, improved communication platforms that increase the effectiveness of the employees. It may also pull staff into a fixation that distracts efficiency in addition to straining the capital of the company.

Empirical Review

A study of the literature shows a plethora of research on social media usage. Studies can broadly be grouped into two; those that focus on social media usage and employee performance and those consider the impact of social media usage on academic performance of studies.

Bernard and Dzandza (2018) looked at the impact of social networking on student academic success at Ghanaian Universities. A sample size of 200 students was randomly selected from eight (8) halls. The authors designed a questionnaire by bringing out statements that participants had to score using a Likert 5-point scale to show their degree of consensus or disagreement. Study results revealed that university students are well exposed to social networking channels particularly on Facebook, WhatsApp platforms, twitter, and instagram. The study findings have showed that the students’ predominant usage of social media for academic purposes is primarily to disseminate information to their colleagues. So social networking supports students by linking them on tasks and class projects to each other. Results of the study showed respondents believe that participating in social networking learning channels improves their comprehension of subjects addressed in college. The researchers therefore established benefits of higher education students’ use of social networking that included strengthening interactions, increasing learning engagement, providing customized course content, and developing teamwork skills. But the research outcome showed that online networks were taking their interest from their research.

Adzovie, Nyieku and Keku (2017) examined the impact of facebook use on the efficiency of Ghana ‘s employees. The study outcome indicates that the amount of time spent, and the number of days people visit facebook has both beneficial and detrimental effects on workers performance. Facebook has since appeared to be an integral aspect of people’s lives. The research also found that Facebook usage during working hours has a major impact on the efficiency of the employees. Thus, workers facebooking impacts both their abilities, expertise and efficiency. This study’s analytical research has shown that workplace utilization of social networking increases workplace efficiency. Organizations can also enable workers to use social networking to improve staff engagement, facilitate the exchange of feedback from staff, embrace creativity and strengthen client connections to maximize employee efficiency.

Nyamanya (2017) examined the connection between social media use and efficiency of workers at Kenya’s public universities. The research focused on the impact on workplace results of Facebook, LinkedIn, Twitter and WhatsApp. The study employed Binary regression model to determine a linear relationship between the independent variables and the dependent variable. Results from the paper showed that Twitter, LinkedIn and WhatsApp impact the efficiency of the workers. The research outcome also suggested that communication, information exchange, job development and partnerships were due to employee performance connected with LinkedIn.. The study recommended introducing clear and detailed measures to tackle the use of social networking, significance, web constraints and security of knowledge, as well as allocating additional funds to educate social network staff in public bodies.

The effect of social networking on employee morale and organizational success at Econet Wireless in Zimbabwe was investigated by Kandiero, Perpetua and Jagero (2014). The study primarily investigated the relation among social networking and efficiency of workers, as well as organizational efficiency. The research used was questionnaire survey methodology, utilizing a sample size of 140 participants. Four sites like Twitter, MySpace, Facebook and LinkedIn were used by the investigators when data analysis and interpretation, frequencies, percentages and mean were used and the knowledge was displayed using tables, graphs and charts. The paper’s results showed that the market opportunities and advantages of employee social networking are both highly underappreciated and undervalued. While several companies around the world have begun to adopt some of the aspects of social networking technologies and enjoy the business advantages, the thoughts that still rule many companies are uncertainty, reluctance and risk. The main results were social networking offers improved rates of efficiency for the workers. Business benefits and advantages of workplace social networking are still underappreciated and undervalued; social networking technology can facilitate enhanced productivity in the workplace by improving staff communication and collaboration, helping to transfer knowledge and making organizations more agile as a result. The study result concluded that this technology can be used to facilitate cooperation among individuals who share a shared interest or target. Increased collaboration would promote sharing of knowledge among individuals, with potential effects of increased productivity.

METHODOLOGY

This section gives a description of the materials organized, methods and procedures followed in directing the study. It provides an overview of the study design, target population, survey methods, and sampling. It also contains information on the applied data sets, data processing tools, and data analysis procedures.

Research Design

Research design is the conceptual structure underpinning the study project which helps the researcher to obtain data to answer the research questions. This study is quantitative in nature and adopted descriptive survey design to investigate the associations amongst the constructs. The choice of descriptive social survey design was informed by, and consistent with majority of previous empirical studies (as in the studies of Varghese  & Kumari, 2018; Usrof, 2017; Luke & Meranga, 2017; Abdullah & Panneerselvam, 2019; Cetinkaya & Rashid, 2018; Adzovie et. al., 2017; Owusu-Acheaw & Larson, 2015; Adzharuddin & Kander, 2018; Kandiero, Perpetua & Jagero, 2014). As noted at the introductory section, the goal of this work was to determine how utilization of social media affects job performance of staff of Wa Polytechnic now Dr. Hilla Limann Technical University. Specific focus of the study is the effects of access to social media channels, and frequency of utilization on performance. With these objectives in mind, adopting the descriptive survey design was deemed relevant as it can facilitate addressing the research questions and help with the achievement of the objectives being investigated. Adoption of the descriptive study design was equally motivated by the data collection instrument used. Thus, employing structured questionnaire for the collection of data makes the study more quantitative, consistent with prior research (Cetinkaya & Rashid, 2018; Adzovie et. al., 2017; Luke & Meranga, 2017; Abdullah & Panneerselvam, 2019; Varghese & Kumari, 2018; Usrof, 2017; Amponsah & Ganga, 2017) which utilized questionnaires for quantitative analysis using basic descriptive and inferential statistics.

Study Population

Similar to Adzovie et al. (2017) and Luke and Meranga (2017), the population of this study comprises academic and non-academic staff of Wa Polytechnic now Dr. Hilla Limann Technical University. The academic staff are instructors, assistant lecturers, lecturers and senior lecturers. The non-academic staff comprises administrative staff, and the junior staff members. Available data from the Human Resource Department of Wa Polytechnic now Dr. Hilla Limann Technical University shows a total academic staff of 110, while the non-academic staff is 64. The sum of the academic and non-academic staff members (174) constitutes the population of this study.

Sample Size Determination Procedure

Given the time constraints, the researcher could not study the entire population. There was therefore the need to determine the appropriate sample size for this study since it is practically impossible to study the whole population within the limited space of time. In deciding the sample size to use, this study applied the sample size determination technique introduced by Miller and Brewer (2003) which is stated in equation 1

where ‘N’ is the sampling frame or the population from which the sample is drawn (174); ‘n’ is the sample size to be determined, and ‘α’ is confidence interval (calculated at a 0.05 significance level). Applying this statistical procedure, the sample size for this study was estimated at 121, approximated from 121.25. The basis for taking the nearest whole number of 121 as the sample size is for the purposes of convenience and simplicity of analysis. The sample of 121 is a fair representation of the population as it represents 69.5 percent of the study population.

Sampling Procedures

In determining the survey respondents this research implemented proportional stratified random sampling methodology. If the survey consists of two homogeneous classes (strata), it is only fitting that the participants are drawn from each category to create a reasonable representation of the population. By having academic and non-academic staff members forming the population, there was the need to select the final sample based on the respective proportions of the staff categories. The 110 academic staff constitutes 63 percent of the population, while the 64 non-academic staff makes up 37 percent of the population. Taking 63 percent of the 121 sample size gives 76 academic staff members while 45 non-academic staff members are also considered. Out of the 121 sampled 98 were those who responded as discussed in Table 7.1.

Individual members in each of the two groupings were then selected by applying simple random sampling, making sure that every member has equal chance of being included in the study. Often the purpose of basic random sampling was intended to reduce sampling bias and error (Kothari, 2004), and as a quantitative study, it is fitting that a probability sampling procedure is applied (Flick, 2015). The application of proportional stratified random sampling technique in this study is consistent with Bernard and Dzandza (2018) who applied random sampling to select respondents from 8 halls in a Ghanaian university. The sampling approach for this study is however more appropriate than the purposive sampling used by Adzovie et al. (2017) whose study focused on both academic and non-academic staff respondents.

Sources of Data 

In line with prior research, this study used primary and secondary data for the investigations. The complexity of the issue under review allowed the usage of primary data more suitable than secondary data sources. The data needed for performing the study was in no way open to the public, nor was it accessible from any organization or agency’s websites or official records. The research questions that influenced this study’s focus have also determined the study’s use of primary data. The literature survey on the subject also offered reasoning for the usage of primary data. Consistent with Adzovie et al. (2017) and Luke and Meranga (2017), data was obtained from both academic and non-academic staff of Wa Polytechnic now Dr. Hilla Limann Technical University.

Data Collection Procedure

For this analysis, the data collection survey approach was implemented and used. In general, data collection survey approaches include information-raising through the use of participant questions to obtain responses. Given the complexity of the topic, the cross-sectional field survey approach is suitable for this research and it tends to be the most commonly utilized data collection technique in social media studies and the performance of employees (Kandiero, Perpetua & Jagero, 2014; Adzharuddin & Kander, 2018; Adzovie et. al., 2017; Usrof, 2017; Varghese & Kumari, 2018; Cetinkaya & Rashid, 2018; Abdullah & Panneerselvam, 2019; Luke & Meranga, 2017) and related studies in the literature (Owusu-Acheaw & Larson, 2015; Bernard & Dzandza, 2018; Mingle, 2015).

Research Instrument

As stated in the preceding segment, this study used organized self-designed questionnaires to gather the data. The investigator directly delivered the questionnaire inside the Wa Polytechnic now Dr. Hilla Limann Technical University campus to the intended participants in their offices and other workplaces. The questionnaire’s architecture and composition are influenced by the research aims, and the study’s related questions. Selected statements or constructions were also steered by empirical literature.

The questionnaire contains six sections, A, B, C, D, E and F.  The section A deals with questions relating to demographic characteristics of respondents. Section B contains questions on the dependent variable (staff performance measures). Section C deals questions on objective one (access to social media channels) of the study. Section D captures questions on objective two (frequency of social media usage). Section E holds questions on objective three (social media usage during working hours). Section F contains questions on the control variable constructs, mainly internal and external factors that can equally affect employees’ work performance.

The nature of questions used in designing the questionnaire was both close-ended and open-ended. A close-ended, five-point Likert scale design of questions was employed in sections B, C, D and E of the questionnaire. Questions in these sections were in the form of statements that reflect the degree of agreement or disagreement of respondents ranging from 1 to 5 for strongly agree, agree, not sure, disagree and strongly disagree respectively. Questions in section A were of both open-ended and closed-ended type. In all, the questionnaire contains 6 constructs for the 6 variables, and 18 statements covering all the constructs.

The bias associated with the five-point Likert scale questionnaire is minimal in terms of the measures of the descriptive statistics, and that the five point scale provides correlation coefficient estimates usable to establish linear association ships among the variable constructs. This suggests the usage of the five-point Likert scale questionnaire is optimal for this research because it can be used to produce concise and inferential statistics for field data analysis.

Model Specification and Estimation Strategy

There are three objectives in this study. Data on all these objectives are analyzed using descriptive and inferential statistics. To accomplish this task, the researcher conceptualized that, based on literature; staff performance is influenced by access to social media channels, frequency of social media usage and social media usage during working hours. Apart from these social media related variables, the study agrees with literature that employee performance is directly affected by other internal and external factors (Lu, Gao, & Chen, 2015; Mericuz, 2015; Sani & Maharani, 2015) which can define the extent of employee efficiency on the job (Haque, Aston & Kozlovski, 2018). As a result, the study introduces these internal and external factors in the operational models as controls in order to determine the real impact of social media usage on staff performance. The functional notation of the above conceptualization is therefore given below

Staff Performance = f (access to social media channels, frequency of social media usage, social media usage during working hours, knowledge and skills, incentives) (1)

Assuming a linear relationship among the variables, the baseline linear multiple regression model incorporating the variables of this study is then constructed from equation 1 as follows

From model 2, SP denotes staff performance measures;  is a constant term that represents the value of SP if each of the independent variables in the model is equal to zero (0). to represent the parameters or the coefficients of the independent variables for estimation. ACCESS represent access to social media channels, FREQ is frequency of social media usage, USAGE stands for social media usage during working hours; KAS denotes employees’ knowledge and skills while INCENTIVES represents inducements available to employees in the workplace. The above model is estimated by the method of ordinary least squares (OLS).

Measurement of Variable Constructs and Expected Outcomes

This section highlights how the variable constructs were measured in this study. From the preceding section, six variables, including the dependent variable, were incorporated in the baseline model, and their definitions and measurement scales are provided in the subheadings following.

Dependent Variable

Following prior research (Abdullah & Panneerselvam, 2019; Cetinkaya & Rashid, 2018; Varghese & Kumari, 2018; Adzharuddin & Kander, 2018;), this study used staff performance as the dependent variable. Staff performance has been defined in various ways including the ability of workers to be aware of their given tasks, meeting expectations and accomplishing allocated ends for the institution. Worker performance encompasses the outcomes, values and achievements expected of an employee in a particular occupation. This study adopts the Guegan, Nelson, & Lubart, 2017 definitions and measures employee performance using the dimensions of contextual performance, adaptive performance and creative performance (Catalsakal, 2016; Guegan, Nelson, & Lubart, 2017; Uryan, 2015), consistent with the work of Abdullah and Panneerselvam (2019). Using the five-point Likert scale questionnaire, statements reflecting these job performance dimensions were used to measure job performance of respondents of this study.

Independent Variables

Three independent or operational variables are used in this study.  These are the social media-related variable constructs of access to social media channels, frequency of social media usage, and social media usage during working hours. The choice of these variables is consistent with key empirical studies in the literature (Abdullah & Panneerselvam, 2019; Varghese & Kumari, 2018; Adzharuddin & Kander, 2018) that selected similar social media usage constructs in their studies. These variable constructs were measured using the five-point Likert scale questionnaire containing statements showing aspects of social media utilization. The study expects that access to social media platforms can have positive but insignificant influence on job performance, while the frequency of usage of social media can have either positive or negative influence on job performance. Utilization of social media in the workplace during working hours is also expected to have either positive or negative effect on job performance.

Control Variables

Two control variable constructs including one internal factor (knowledge and skills) and one external factor (incentives) were used. These variables were measured using the five-point Likert scale questionnaire. These variables are expected to have positive influence on employees’ job performance.

Data Analysis Methods

Data analysis in this study followed several specific steps and activities. The first was that field results obtained from study participants were analyzed by ensuring that the respondents ‘ answers were accurate and complete. This has been accomplished through sorting and editing. The object of editing was to review the data to guarantee that there were no differences in participants’ answers.

The first phase as explained above was preceded by the next stage that involved coding frame design, or a coding scheme. The method was then used to convert the Questionnaire answers into numerical statistics. In this process, the composite figures for each of the constructs were obtained by transforming the individual numbers into averages. In other words, the aggregate figure for each construct was used by computing averages for all the 6 construct variables.  The next stage in the process was to enter the aggregate figures in the data processing software. Quantitative data basically in the form of descriptive statistics (measures of central tendencies and measures of dispersion) were then generated from the software after commands were issued. The SPSS software (version 20) was used to generate the frequencies and percentages for the demographic data while the E-Views statistical software (version 9) was used to generate the descriptive and inferential statistical values. Then the actual data analysis activity ensued where the quantitative data was analyzed using the descriptive statistical properties such as the mean, median, maximum, minimum and standard deviation and the inferential statistics of correlation coefficients and regression coefficient estimates.

RESULTS AND DISCUSSION

This study sought to scrutinize the effects of social media usage on staff performance in Wa Polytechnic now Dr. Hilla Limann Technical University. This chapter entails submitting the results obtained from field data and discussing the findings in line with the reviewed literature. Although 121 sets of questionnaires were administered to respondents, 100 were returned with 98 of them useful for analysis, producing a response rate was 79.8 percent. The remainder of the chapter is partitioned as follows. Section 4.2 gives information about the demographic features of the respondents. Section 4.3 entails the description of the correlation results of the variable constructs. Section 4.4 informs us of the OLS regression outcome. Sub-sections 4.4.1 to 4.4.3 present the results and discuss the findings in line with the objectives of the study. Section 4.5 provides a summary for the chapter.

Demographic Data of Respondents

The data used for the analysis of the results were made available through the research participants. As required, the demographic features of the research respondents are disclosed. The fallouts of field data assembled on the demographic properties of the research subjects are illustrated in this section in Table 7.1. The demographic data relevant for the purpose of this study include age classification, gender composition, educational level, position of respondents, years of work, years of using social media and respondents’ most utilized or preferred social media platform.

Table 7.1: Demographic data of respondents

Source: Field data, 2020

The fallout of from Table 7.1 points out that the age classification has it that as many as 48 respondents who make up the majority or 49 percent of the respondents were within the age bracket of 40 and 49 years. This is trailed by 26 persons who were between the ages of 30 and 39 years, taking 26.5 percent of the overall sample respondents. It can further be averred that respondents who were aged between 18 and 29 years take the third position with a sum percentage of 14.3 percent. The last 10.2 percent of the respondents are the batch of the subjects who fell between the ages of 50 and 59 years.

The gender composition of the study’s respondents illuminates the dominance of male (62) over female (36) with corresponding percentages of 63.3 and 36.7. For the level of education, original field data portrays that the overwhelming majority of respondents (56) who proportionally represent 57.1 percent are holders of master’s degrees, shadowed by 18 staff members (18.4 percent) who are in possession of bachelor’s degrees. PhD holders come next with 16.3 percent as compared to 8.2 percent for HND holders.  Pertaining to the work position of the sampled contributors, the outcome of field data leads us to recognize that 60 persons or approximately 61.2 percent of the sum number of persons were senior members of the Wa Polytechnic now Dr. Hilla Limann. This categorization includes senior members in academic and non-academic positions (i.e teaching and non-teaching staff roles). Similarly, the 22 officers in senior staff portfolios, making 22.4 percent, include teaching and non-teaching staff members. Those falling within the junior staff category constitute the last 16.3 percent.

The study further probed the workers’ years of work, and it came out those 52 respondents or equivalently 53.1 percent of the workers engaged had spent between 6 to 10 years working in various capacities. The next 24 respondents (24.5 percent) had been working for more than 1 year but less than 6 years, while the last 22 persons (22.4 percent) have had more than 10 years working life spent at the Wa Polytechnic campus now Dr. Hilla Limann Technical University.

The last but one item is the number of years respondents have experienced using social media for varying reasons. It turn out that 46.9 percent of the members of staff engaged have been using social media over the past 6 to 10 years, whereas the next 34.7 percent have been using social media over the last 1 year to 5 years. Then, 18 staff members or 18.4 percent of the respondents have used social media for more than 10 years. The implication of these statistics for this study is that respondents generally appear to be quite familiar with social media usage, and therefore, they can contribute to making relevant information that can help address the research questions and meet the objectives of the paper.

Lastly, the study explored which social media channel is the most used by the respondents who took part in the investigation. Data reported in Table 7.1 provides confirmation that WhatsApp, Facebook and Twitter are the first three social media platforms most utilized by the respondents. The respective percentages of respondents who affirmed using WhatsApp, Facebook and Twitter are 30.6, 27 and 20 in approximate terms. The statistics further show that 12.2 percent of the sampled respondents use YouTube as opposed to 4.1 percent for LinkedIn. The indication from these figures is the picture being painted about the extent of popularity of social media channels among workers of the Wa Polytechnic now Dr. Hilla Limann Technical University.  This, to a large extent, justifies or offers support for the purpose of this study which is exploring the influence of social media usage on staff performance.

Correlation coefficients of the variable constructs 

Table 7.2 offers information about the correlation coefficients of the variable constructs under study. The statistical significance associated with the correlation coefficients are taken by the probability numbers put brackets. The correlation coefficients are at reduced rank, taking up to three decimal places. This study sets the limits for the correlation coefficients to be at 0.8. This means that any coefficient exceeding the 0.8 mark is deemed as exhibiting high correlation. A critical look at the statistics in Table 7.2 shows that all the pairs of variables have relatively lower correlation numbers less than the benchmark of 0.8. The highest correlation coefficient is 0.429 for knowledge and usage of social media forms. It may also be seen that all the correlation coefficients are positive and statistically significant at 1 percent. The only negative correlation coefficient is the one prevailing between incentives and frequency of social media usage (-0.056). Based on these correlation statistics, it is concluded that multicolinearity is absent in the field dataset.

Table 7.2: Correlation Analysis

Source: Field data, 2020. Notes: * represents statistical significance at 1%.

OLS Regression Outcome

The core motive of this empirical study was to conduct an investigation into how social media usage affects work performance of staff of Wa Polytechnic now Dr. Hilla Limann Technical University.  This section submits the results of field data estimated for the purpose of achieving this objective. The OLS model is characterized by R-squared of 0.499377 and an Adjusted R-squared of 0.485614. The F-statistics stood at 36.28445 with a probability of 0.000000, significant at 1 percent. These figures suggest that approximately 50 percent of the variation in the dependent variable is jointly explained by the independent variables in the model, and that the model is quite well specified with the predictive power of the model averagely good. The Durbin-Watson test statistic stood at 1.977299, indicating the absence of serial autocorrelation in the model. The OLS results are posted on Table 7.3. The dependent variable is staff performance. The information provided in Table 7.3 is used for the analysis of all the three objectives of the study.

Table 7.3: OLS regression output. Dependent variable is staff performance

Source: Field data, 2020

Access to Social Media Channels and Staff Performance

This section submits the results of field data processed for objective one of the study. This objective centered on the examination of how access to social media channels contributes to staff performance. The OLS regression output in Table 7.3 has it that access to social media platforms in the workplace imposes a positive influence on staff performance. The statistical coefficient of access to social media channels stands at 00.064969 with a numerical probability value of 0.1602. In statistical terms, this coefficient is not significant, implying that having access to social media instruments does not in itself translate into actual utilization, and therefore cannot hold any connotations for staff job delivery. Thus, this result suggests that all else identical, the accessibility of social media channels in the workplace as in internet connections and Wi-Fi services and unrestricted use of mobile devices can have implications for work output of employees, but this result is not statistically supported. In the end, we can deduce that mere access to online media tools does not in itself mean usage, and for that reason, we cannot proffer statistical association ship with access and job performance.

Outcome of data reported in this section lends support to that of previous studies. First, the proportional result is in association with Kandiero et al (2014) who found that social networking facilities offer improved productivity in the workplace. Mahboub (2018) found that using social media infrastructure in the banking sector positively contribute to reaping financial and non-financial outcomes through enhanced marketing activities, and customer satisfaction. Although none of the reviewed prior studies directly examined the impact of access to social media facilities in the workplace on employees’ performance, having found parallel movement for usage of social media and workers’ output, suggests implicitly that availability and access to social media infrastructure represent a precondition for usage. Positive outcome recorded in this study therefore give credence, credibility and substantiate that of the prior studies (Kandiero et al, 2014; Mahboub, 2018).

Frequency of Social Media Usage and Staff Performance

The second objective of this study deals with determining whether or not the frequency of usage of social media devices have effects on staff performance. Measuring frequency of usage in terms of the number of times of daily visit to social media sites while at work, and the number of hours spent on social media sites while at work, the estimated result illustrated in Table 7.3 confirms the expectation of this study. The outcome from the estimated regression line shows that frequency of social media usage has a positive significant influence on staff output of work. Frequency of usage as a variable inserted a parameter number of 0.234348 in the mathematical expression connecting between frequency of utilization and staff performance. The corresponding probability standing was 0.0000, highly significant at 1 percent. The intuition behind this result is that if the work of all the other variable constructs in the model are assumed unaltered, then a unit increase in the hours spent and the number of times visiting online social media places can contribute to 0.234348 change in the performance of staff. The result seem quite realistic in the sense that once users spend part of working hours going online, it can be argued that irrespective of the purpose of the online visit, performance can either be adversely altered or be absolutely enhanced. The reported result appears to point explicitly to a possible performance enhancement due to frequency of social media usage. In reality or in practical terms, when one visits offices and finds workers hooked onto their WhatsApp or Facebook pages, at the expense of serving clients, one can only conclude, even without any statistical proof, that such practices can be detrimental to output. The result found here rather offers arithmetical evidence that it is not always the case that when employees go online while in the office, their performance becomes negative. This evidence has the potential to alter perception about employees’ online visits while at work.

The finding relative to the frequency of usage of social media is consistent with Adzovie et al (2017) who found that the time spent and the number of times of visiting social media platforms adversely affects output of workers in the University of Cape Coast.

Effect of Social Media Usage on Staff Performance

The third and last objective of the study sought to ascertain the effect of social media usage during working hours on staff performance. Results from data are conveyed in Table 7.3. It can be observed that social media usage has a numerical coefficient of 0.077476 with a likelihood number of 0.0705, gaining a 90 percent confidence interval or a 10 percent significant level. The connotation of this econometric outcome is that even if we take it that the impact of all the other variables remains stable, then a unit variation in the usage of social media forms during working periods will induce staff performance to improve by 0.077476.  By way of construing this outcome, what this result brings on board is that if workers engage in social media activities involving finding and sharing of information, exchanging experiences and learning new ideas and competencies, then their performance may be enhanced. Equally significant inference that can be extracted from the outcome is that social media platforms offer a unique way for people to remotely collaborate, network and cooperate with colleague professionals for discussions of issues of common interest without necessarily meeting face-to-face during official hours. In this case being able to have virtual interface with people from different geographical locations affords the opportunity for the contemporary worker to learn new skill sets and build capacities at relatively cheaper cost.

The result additionally affirms that using social media can encourage workers to work from home, and thus execute assigned tasks even outside official hours, a feature that can contribute to continuous performance of employees. In restrictive circumstances such as the outbreak of the coronavirus pandemic, the application of social media technologies in the delivery of educational services comes in demand, enabling normal services to be conveyed and accessed from virtual sites. The use of social media to solve work-related problems in the course of working is embedded in the result produced by this study. Individuals having challenges with the execution of tasks can link up with experts for insights and directions.

Comparing the outcome of this study to that of prior research one can observe insightful similarities. For example, outcome from data corroborates with a number of previous studies including who found proportional inter-linkages for social media usage and employee job performance in Malaysia University. The work of Luke and Meranga (2017) show that utilization of social networking sites has substantial power on work outcomes of the university staff in Indonesia. Further, Abdullah and Panneerselvan (2019) documented direct association ship for patronage of social networking platforms and employees’ performance in the judicial sector of Malaysia.  Likewise Cetinkaya and Rashid (2018) reported of a parallel movement between utilizing social media systems and job output of workers in the services industry in Turkey, just as Varghese and Kumari (2018) found in education sector in India that using social media in course of working and for the purposes of work, improves workers’ output. Notwithstanding numerous studies discovering positive linkages between social media usage and employees’ performance, Usrof (2017) found that using social media platforms can be a source of employee non-performance and by extension inefficiency in corporate resource utilization. Adzovie et al (2017) observed that both positive and negative outcomes can be obtained from social media uses in the workplace.

The parameter figures for the two control variables show that both employees’ knowledge and skills and incentives positively contribute to performance. Knowledge and skills imputed coefficient of 0.220536 in the mathematical equation and generated a probability numeral of 0.0000, which is significant at 1 percent. Incentives also had a coefficient of 0.074156 and a probability numeral of 0.512, significant at 10 percent. The suggestion from these figures is that even without the use of social media forms, employees’ performance can be explained by their level of knowledge and skills acquired, and also by the provision of incentives, for the execution of their jobs. Having the right knowledge and skills for a particular job can be a pre-requisite for job delivery, and being motivated externally can form a reasonable basis for performance. The implication therefore is that frequently applying social media tools in the delivery of one’s job must be complemented with the quality of knowledge and skills, along with welfare systems through motivational packages.

SUMMARY OF FINDINGS

Against the popularity of social media usage among contemporary people, and the need to promote employee performance, this study was meant to investigate the influence imposed by social media utilization and employee performance in the Wa Polytechnic now Dr. Hilla Limann Technical University.  This study was conducted on three objectives, and the results and finding obtained for each are explained in the subsequent paragraphs.

The first objective relates to the examination of the effect of access to social media channels on staff performance. The results revealed a positive but statistically irrelevant influence of access to social media forms on employee performance. Therefore, availability and accessibility of social media tools in the work premises represent the first stage of the discussion and do not necessarily contain repercussions for employees’ job performance.

Objective two of the study examines the influence of frequency of social media usage on performance. Empirical evidence creates a statistically significant direct consequence for staff performance from frequency of social media utilization. This result leads to the discovery that frequency of social media usage has a significant positive impact on workers’ performance. Hence, the perception that frequent utilization of social media while at work can have disastrous consequences for employee performance is refuted or not supported by this study.

The last objective considers the effect of social media usage on staff performance. After going through the analysis of the data, the author found a significant positive effect of social media usage on staff performance. This finding is embedded with potential implications for institutional policy drawings and actions, enumerated in the concluding section of this chapter.

The findings from data on the control variables of employees’ knowledge and skills and incentives revealed arithmetically significant association ships with employees’ performance. Thus, the complementary roles of knowledge and skills possessed by workers and the offer of incentives for workers in driving employee performance is being shown by this finding.

CONCLUSIONS

Technological advancement has been touted as responsible for the changing interactions among people today. As a consequence, social media usage has assumed the fundamental channel through which individuals connect, engage and collaborate on daily basis. Despite this acknowledgement, and the growth in popularity of social media forms for communication and other purposes, disagreement among scholars regarding social media’s benefit in the workplace prevails. Therefore, literature has failed to offer a categorical position on the matter, even though abundance of studies on the influence of social media on employees’ performance has been conducted globally. Literature in Ghana in particular has focused on how social media usage affects academic performance of students. Very limited, if not absent, studies on the effect of social media usage on employees’ performance in the higher educational institutions can be found in Ghana.

The development of this study was as a result of the inconclusive position of prior studies from across the globe, and the lack of knowledge on the effect of social media usage on performance of workers in higher educational institutions in Ghana. This study therefore sought to investigate the effect of social media usage on the performance of workers of Wa Polytechnic now Dr. Hilla Limann Technical University.  By adopting quantitative approach and descriptive survey design, this study collected primary data using self-designed structured questionnaire from 98 respondents.  Proportional stratified random sampling technique was used. Data analysis was performed with inferential statistics in the form of OLS multiple regression and correlation analysis, complemented with descriptive statistics like percentages. Consistent with some prior studies, the study found positive substantial effects of social media usage and frequency of usage on workers’ performance. However, the effect of access to social media channels on employees’ performance was arithmetically immaterial.

The implications originating from these outcomes are explained as follows. Access to social media infrastructure reinforces its utilization and frequency of utilization, which in the end can drive workers performance in higher educational settings. Therefore, unrestricted access to social media tools can be regarded as a fundamental necessity that can encourage utilization and staff performance. In academic environments, social media access and utilization cannot be compromised, but should be promoted to facilitate teaching, learning, research and the delivery of academic-related and community services. Educational authorities should therefore make investments in internet enabling facilities like Wi-Fi connectivity, and associated infrastructure that can serve as bedrock for social media access and utilization for improved performance of staff.

RECOMMENDATIONS

Based on the findings and conclusion, the following recommendations are made for the consideration of tertiary education institutions in general.

Higher educational authorities are advised to collaborate with telecommunication companies to make arrangements for the provision of free mobile data and relevant facilities that serve as the foundation for making social media accessible to all staff. This move can go a long way to ensure social media tools can easily be accessed by all.

Measures that restrict social media usage in tertiary educational institutions such as the placement of key locks or access passwords on Wi-Fi connectivity should be removed or streamlined for enhanced utilization.

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