Final Proposal FINAL Final

2 The purpose of this study is to shed light on the complex connection between social media usage and efficiency in the setting of a police department. The results of this research might have important consequences for businesses that want to use social media to improve employee and company performance in areas like information sharing, communication, and decision making. This study adds to the larger conversation on how social media affects workplace performance by examining this phenomenon in the context of the Maldives Police Service, and it sheds light on how to make better use of these platforms in business settings.

5 1. Introduction 1.1 Research Background The emergence of social media has revolutionized communication, enabling individuals and organizations to interact, exchange information, and communicate more effectively. With the increasing popularity of social media platforms like Facebook, Twitter, Instagram, LinkedIn, and YouTube, they have now become an indispensable part of people's daily routines. According to Statista, as of 2021, the number of active social media users worldwide stood at 4.2 billion, with projections indicating a rise to 4.4 billion by 2025 (Statista, 2021). The widespread adoption of social media has had far- reaching effects across society, impacting business operations and workforce productivity in particular. Social media usage in the workplace has become a topic of interest among researchers in recent years. A study conducted by Dhir and colleagues in (2018) in the United States found that social media use during work hours can lead to lower job satisfaction, lower organizational commitment, and reduced work engagement. Another study by van den Broeck et al. (2018) found that social media use during work hours can lead to increased fatigue and decreased well-being among employees. Despite these negative effects, social media can also have positive impacts on employee and organizational performance. For instance, social media can improve communication and collaboration among employees, leading to better job performance and increased job satisfaction (Krasnova et al., 2017). According to a study by Krasnova and colleagues, social media can also be used as a marketing tool to enhance brand awareness and reputation, which can positively impact organizational performance. This is in line with the modern advancement in technology and us of technology as business tool. Moreover, the use of social media has become increasingly important for organizations to engage with their stakeholders, including customers, employees, and the

8 1.3 Problem Statement The Maldives Police Service is essential to maintaining order in the nation. It is crucial to guarantee top-notch performance from all staff members for the smooth operation of the business. However, the increasing popularity of social media among Maldives Police Service personnel has prompted worries about its effect on individual productivity and, by extension, the efficiency of the whole organization. The Maldives Police Service, in light of the growing worldwide trend toward incorporating social media into different sectors of society, has to conduct a thorough analysis of the impacts of social media use on its workers and organization to make educated judgments on regulating or controlling its use. The problems that arise from employees using their devices at work are a worldwide phenomenon that has been extensively studied. Global research (Cetinkaya & Rashid, 2018) shows that excessive social media usage in the workplace might have profound implications. Potentially detrimental effects include lower output, lower work satisfaction, and the introduction of distractions that lower productivity. In addition, such unmonitored social media use inside law enforcement organizations may severely impact the organization's image and reputation as well as public trust and confidence. Despite the widespread nature of this problem and the growing body of literature on the effects of social media in various workplace settings, there appears to be a dearth of studies analyzing the effects of social media usage on employee and organizational performance within the Maldives Police Service. The specific problems and possibilities this phenomenon brings in the context of the Maldives need an in-depth investigation. 1.4 Research Aim, Objectives, and Questions The aim of this research is to investigate the impact of social media usage on employee and organizational performance in the Maldives Police Service. The specific objectives are:

11 discovered. Similarly, Boyd and Ellison's (2007) study showed that the link between social media and social capital and relationship building might vary depending on the platform and features employed. 2.2 Theory and Models The theoretical framework for this study will be based on the Social Information Processing (SIP) theory proposed by Joseph Walther. According to SIP theory, social media usage can affect social interactions and relationships. The theory posits that online communication can be just as effective as face-to-face communication in building and maintaining relationships, but it takes longer to develop trust and social bonds in online environments (Walther, 1992). The theory also suggests that social media can enhance social influence, increase self-disclosure, and reduce social anxiety, which can lead to greater job satisfaction and employee performance (Krasnova et al., 2017). In addition to the SIP theory, other studies have explored the impact of social media on employee and organizational performance. For example, a study by Alzougool and Alzougool (2020) found that social media usage can enhance knowledge sharing and collaboration among employees, leading to improved organizational performance. The study also found that social media can increase job satisfaction and employee engagement, which in turn can lead to higher levels of organizational commitment and reduced employee turnover. Another study by Lee and Kim (2020) found that social media usage can have a negative impact on employee performance and organizational productivity. The study found that excessive social media usage can lead to decreased productivity, job satisfaction, and increased absenteeism. This highlights the importance of establishing clear guidelines for social media usage within the workplace, and monitoring employee usage to ensure that it does not negatively impact overall performance and productivity. In addition to the direct impact on employee and organizational performance, social media usage can also have a significant impact on the recruitment and retention of

14 2.4 Recent Empirical Research Several recent studies have investigated the impact of social media usage on employee and organizational performance. A study conducted by Lee and Kim (2020) found that excessive social media usage at work can lead to decreased productivity, job satisfaction, and increased absenteeism. Another study by Krasnova et al. (2017) found that social media usage can improve communication and collaboration among employees, leading to better job performance and increased job satisfaction. Numerous investigations in recent years have centered on examining the influence of social media usage on employee and organizational performance. Among them, Lee and Kim's (2020) research explored how social media use affects employee productivity, job satisfaction, and absenteeism. Their findings revealed that excessive social media usage during work hours can result in diminished productivity, reduced job satisfaction, and increased absenteeism. This underscores the significance of using social media responsibly and establishing well-defined guidelines for its use in the workplace. Another study by Krasnova et al. (2017) examined the impact of social media usage on employee communication and collaboration. The results of this study indicated that social media can enhance communication and collaboration among employees, leading to improved job performance and increased job satisfaction. This suggests that Employee Performance Communication Decision Making Organizational Performance

17 In this case, the population size is approximately 5,000 employees, the margin of error is 5%, or 0.05, and the confidence level is 95%, or 0.95. Plugging these values into the formula, we get: ? = 5000 / (1 + 5000(0.05^2)) ? = 5000 / (1 + 12.5) ? = 5000 / 13.5 ? = 370.37 Rounding up to the nearest whole number, the sample size for this study will be 371. Using a probability sampling technique, the data for this study will be gathered. With probability sampling, every member of the population has an equal chance of being chosen for the sample (Taherdoost, 2016). A straightforward random sampling data gathering strategy will be used for this study. So, with a simple random sample, each employee has an equal probability of being included in the sample (Taherdoost, 2016). 3.3 Data Collection Method To collect data for this study, a self-administered online survey in the form of a Google Form with a Likert scale will be used. This method is convenient and efficient as participants can complete the survey at their own pace and in a convenient location. The survey link will be distributed to employees of the Maldives Police Service through their official email addresses and through Viber, Telegram, and WhatsApp to increase the response rate. Participants will be given a specific time frame to complete the survey, and reminders will be sent to those who have not responded within the designated time. This will help to ensure that sufficient responses are collected for the study. Overall, using an online questionnaire in the form of a Google Form with a Likert scale is an effective and efficient data collection method, allowing for the collection of large amounts of data in a short period while also ensuring high response rates.

14 useful information that I understand. 2. My superior share and respond to information in a timely manner. 3. My superior actively listen to my viewpoints. 4. My superior always speaks politely and this motivates me to model him/her. 5. I know what I am expected to achieve when I am given a task at work. 6. My superior maintains essential information flows to me. 7. I always avoid using harsh language when communicate with colleagues. 8. I use polite language to advise my colleagues. 9. I use appropriate language to address others. 10.I try to interact with colleagues nicely at work. Kowang, Husin & Ping, (2019).

21 11. I always respect my colleagues’ views. 12. I helps someone without being asked decision making 1.Decisions are made by managers individually without much interaction 2.Managers employ consensus oriented team decision making 3. Each department makes decisions more or less on its own, without regard to other departments 4.There is a great deal of interdepartmental interaction on most decisions. Miller & Lee, (2001). Employee employee 1.I receive meaningful recognition for work well Hee, Qin, 15 performance performance done. 2. I receive useful feedback from superior on my job performance. 3. My work has made contribution to the good of the organization would please me. 4. I like to feel that I am making some contribution not for myself but for the Kowang, Husin Ping, (2019).

organization as well. 5. I persist in overcoming obstacles to complete a task. 6. I meet the formal performance requirements of the job. organisational performance organisational performance 1.Customers perceive that they receive their money’s worth for purchasing your products and/or services 2. Your customer retention rate is as high as or higher than that of your competitors 3 Your sales growth rate is as high as or higher than that of your competitors 4.Profitability of your company is good relative to the overall performance of your business sector 5. Your overall competitive position is strong in your business sector Law & Ngai, (2008).

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16 4.0 DATA ANALYSIS 4.1. Respondent Profile Table 1: Demographic Frequency Percent Valid Percent Cumulative Percent Gender Valid Female 72 35.0 35.0 35.0 Male 134 65.0 65.0 100.0 Total 206 100.0 100.0 Age [Years] Valid 18 - 25 years 43 20.9 20.9 20.9 26 -30 years 81 39.3 39.3 60.2 31 - 35 years 41 19.9 19.9 80.1 36 - 40 years 30 14.6 14.6 94.7 41 - 45 years 11 5.3 5.3 100.0 Total 206 100.0 100.0 Marital status Valid Divorced 14 6.8 6.8 6.8 Married 138 67.0 67.0 73.8 Single 54 26.2 26.2 100.0 Total 206 100.0 100.0 Highest Education Level Valid A' Level 47 22.8 22.8 22.8 Diploma 34 16.5 16.5 39.3 O' Level 78 37.9 37.9 77.2 Other 2 1.0 1.0 78.2 Living condition Valid Living Alone 20 9.7 9.7 9.7 Living with Family (husband & wife) 53 25.7 25.7 35.4 Living with parents 52 25.2 25.2 60.7 Sharing Apartment 81 39.3 39.3 100.0 Total 206 100.0 100.0 What is your role within the Maldives Police Service? Valid Administrative Staff 36 17.5 17.5 17.5 Command Staff 33 16.0 16.0 33.5 Investigator/Detective 36 17.5 17.5 51.0 Other 49 23.8 23.8 74.8 Patrol Officer 52 25.2 25.2 100.0 Total 206 100.0 100.0

Working Department. Valid Central Policing Command 19 9.2 9.2 9.2 Corporate Services Directorate 13 6.3 6.3 15.5 Crime Investigations Command 21 10.2 10.2 25.7 Directorate Of Intelligence 7 3.4 3.4 29.1 Forensic Services 9 4.4 4.4 33.5 Local Policing Command 66 32.0 32.0 65.5 Operational Support Command 43 20.9 20.9 86.4 People Directorate 18 8.7 8.7 95.1 Professional Standards Command 10 4.9 4.9 100.0 Total 206 100.0 100.0 How many years have you been working for the Maldives Police Service? Valid 1 – 5 years 63 30.6 30.6 30.6 10 – 15 years 47 22.8 22.8 53.4 5 – 10 years 51 24.8 24.8 78.2 Less than 1 year 23 11.2 11.2 89.3 Over 16 years 22 10.7 10.7 100.0 Total 206 100.0 100.0 Monthly disposable income. Valid Over 25,001 3 1.5 1.5 1.5 Rf12,001 – Rf 16,000 56 27.2 27.2 28.6 Rf16,001 – Rf 20,000 21 10.2 10.2 38.8 Rf20,001 – Rf 25,000 13 6.3 6.3 45.1 Rf4,000 – Rf8,000 21 10.2 10.2 55.3 Rf8,001 – Rf 12,000 92 44.7 44.7 100.0 Total 206 100.0 100.0 How often do you use social media for work purposes? Valid Always (daily) 60 29.1 29.1 29.1 Never 8 3.9 3.9 33.0 Occasionally (less than once a week) 42 20.4 20.4 53.4 Often (4-6 times per week) 32 15.5 15.5 68.9 Sometimes (1-3 times per week) 64 31.1 31.1 100.0 Total 206 100.0 100.0

The demographics of the respondents provide light on the makeup of the whole sample. A statistical data analysis indicates that male respondents comprised 65% of the model, while females accounted for 35%. This might be significant to the research because it may reflect differences in how men and women approach and engage with social media in the workplace (Hee et al., 2019). The data represents a wide range of ages, with the most significant group being those aged 26-30 (34.3%). This shows that there may be considerable differences in social media use habits among age groups, each with the potential to have a unique effect on employee and organizational performance. Sixty-seven percent of respondents were married, 26.2% were never married, and 6.8% divorced. The personal duties of a married person may vary from those of a single or divorced person, which may affect the amount of time an employee spends on social media. Moreover, the majority of respondents (25.2%) said they were patrol officers, followed by administrative workers (17.5%), investigators/detectives (17.5%), and others (23.8%) in the Maldives Police Service. Investigative positions inside a company may be more reliant on internet communication and research than other positions due to their responsibilities (Fakhr et al., 2019). In addition, respondents worked in a wide variety of departments, with the Local Policing Command accounting for the most significant share (32.0%). A worker's ability to join social media discussions at work and utilize their accounts for official business may be contingent on the division in which they are employed. In addition, a sizeable percentage of respondents said they "Always" (29.1%) or "Sometimes" (31.1%) use social media for work-related objectives. Therefore, they consider social media an essential and valuable resource in their work. There may be a difference in the use of social media for professional responsibilities since a tiny fraction (3.9% to be exact) said they "Never" use social media for work. Some respondents use social media "Often" (15.5%), while others use it "Occasionally" (20.4%). Possible explanations for the varying use rates include variances in work responsibilities, departmental needs, and personal

preferences. As more regular use may have different consequences than occasional or infrequent use, understanding these patterns is vital for studying the influence of social media on individual and organizational performance. 4.2. Normality Table 2: Descriptive Statistics N Mean Std. Deviation Skewness Kurtosis Statistic Statistic Std. Error Statistic Statistic Std. Error Statistic Std. Error KS 1 206 3.71 .065 .927 -.475 .169 .420 .337 KS 2 206 3.74 .069 .997 -.735 .169 .440 .337 KS 3 206 3.69 .067 .957 -.601 .169 .436 .337 KS 4 206 3.71 .069 .984 -.566 .169 .223 .337 KS 5 206 3.85 .069 .987 -.688 .169 .284 .337 KS 6 206 3.84 .069 .990 -.718 .169 .303 .337 KS 7 206 3.95 .065 .928 -.782 .169 .719 .337 COMM 1 206 3.82 .066 .945 -.674 .169 .405 .337 COMM 2 206 3.78 .064 .924 -.449 .169 -.077 .337 COMM 3 206 3.77 .066 .948 -.673 .169 .652 .337 COMM 4 206 3.81 .068 .972 -.739 .169 .440 .337 COMM 5 206 3.89 .063 .904 -.626 .169 .278 .337 COMM 6 206 3.79 .068 .969 -.793 .169 .641 .337 COMM 7 206 3.87 .069 .986 -.811 .169 .603 .337 COMM 8 206 3.90 .071 1.019 -.939 .169 .811 .337 COMM 9 206 3.92 .069 .997 -.699 .169 .104 .337 COMM 10 206 3.92 .069 .990 -.605 .169 -.144 .337 COMM 11 206 3.94 .063 .909 -.633 .169 .403 .337 COMM 12 206 3.84 .064 .919 -.868 .169 1.073 .337 DM 1 206 3.29 .064 .923 .065 .169 -.095 .337 DM 2 206 3.24 .070 1.006 .118 .169 -.251 .337 DM 3 206 3.07 .062 .892 .283 .169 .230 .337 DM 4 206 3.06 .067 .968 .329 .169 -.135 .337 EP 1 206 3.16 .066 .947 .301 .169 -.146 .337 EP 2 206 3.26 .069 .987 .221 .169 -.373 .337 EP 3 206 3.26 .068 .981 .277 .169 -.286 .337 EP 4 206 3.35 .072 1.034 .289 .169 -.843 .337 EP 5 206 3.32 .071 1.018 .287 .169 -.676 .337 EP 6 206 3.27 .069 .993 .287 .169 -.587 .337 OP 1 206 3.33 .069 .992 .193 .169 -.501 .337 OP 2 206 3.19 .067 .967 .299 .169 -.377 .337 OP 3 206 3.11 .068 .969 .261 .169 -.291 .337 OP 4 206 3.11 .066 .946 .342 .169 -.164 .337 OP 5 206 3.07 .067 .955 .270 .169 -.137 .337

Valid N (listwise) 206 Skewness and kurtosis statistics were used to assess the out-of-the-ordinary behavior of these variables. Skewness between -2 and +2 and kurtosis between -7 and +7 are often considered acceptable for a normal distribution (Taherdoost, 2016). Most of the study's variables do not follow the normal distribution, but they do so within a reasonable margin. A positive skewness score, as shown in KS 2 and COMM 7, indicates that some respondents evaluated this component of social media usage higher than the mean, which may have a beneficial effect on performance. Variables like EP 4 and OP 5 showed negative skewness values, indicating that some respondents ranked these items lower than the mean, which may indicate a negative influence (Oyewobi et al., 2021). Importantly, certain variables in this research had kurtosis values that are beyond the usual range of -7 to +7, suggesting that the data points outside this range considerably vary from a normal distribution. These findings might be the result of peculiarities in the data or the study subjects. Scientists looking into the effect of social media usage on productivity in the workplace should, therefore, think about using suitable statistical approaches that do not depend on the assumption of normalcy. 4.3. Reliability Table 3: Reliability statistics Cronb ach's Alpha Cronb ach's Alpha Based on Standardized Items N of Items Whole scale .966 .966 34

KS .932 .932 7 COMM .961 .961 12 DM .891 .891 4 EP .939 .938 6 OP .919 .920 5 According to Kang et al. (2008), a Cronbach's Alpha value above 0.7 is considered acceptable for research purposes, and the overall Cronbach's Alpha for the scale is.966, indicating that the reliability of the scale is quite high. The scale consists of multiple items that measure distinct components, and the high Cronbach's Alpha value suggests a strong internal consistency among these items. Concerning the sub-scales, the Cronbach's Alpha value of.932 for the "KS" (Knowledge Sharing) construct confirms the reliability of the questions used to evaluate knowledge sharing behaviors. This sub-scale consists of seven items. Cronbach's Alpha for the "COMM" (Communication) construct is.961, indicating high internal consistency and supporting the validity and reliability of the twelve items used to evaluate the organization's social media communication strategies. While a Cronbach's Alpha of.891 indicates a fair amount of internal consistency for the "DM" (Decision Making) construct, this also calls for a closer look at the components within this framework to identify potential sources of inconsistency and make the necessary improvements. The components making up the "EP" (Employee Performance) and "OP" (Organizational Performance) constructs are highly consistent with one another, as shown by their respective Cronbach's Alpha values of.939 and.919. Because of this, it is clear that the evaluation techniques used to determine how social media affects productivity at the individual and organizational levels can be trusted. 4.4. Validity The study's validity analysis provides convincing evidence that the data are suitable for factor analysis. Determinant value

Determinant value = 1.082E-17 A determinant value of 1.082E-17 in the validity evaluation provides strong support for the idea that the data is suitable for factor analysis. Our multivariate dataset relies heavily on this determinant value, which confirms that the variables under consideration have low collinearity. We may be confident in our results since a factor of this size suggests that the relationships between these variables are not linear. This finding has important significance for our study because it highlights the unique and separate character of the elements connected to social media use, employee performance, and business success. Our research adds to an in-depth and complete knowledge of this critical modern problem by carefully identifying the varied and various effects of social media on persons and businesses without compromising on the acceptable determining value criteria. In this scenario, a determinant value close to zero shows that the variables are not linearly correlated and do not display multi-collinearity difficulties, albeit the acceptable range for determinant values varies on the individual context and analytical methodologies utilized (Kang et al., 2018). This provides further support for the notion that the research investigates the distinct impacts of social media on a variety of dimensions without being hampered by collinearity. KMO and Bartlett's Test Table 4 Table 4 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .936 Bartlett's Test of Sphericity Approx. Chi-Square 7533.054 df 561 Sig. .000 The outcomes of the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's Test of Sphericity are used to evaluate the impact of social media usage on individual and organizational performance. The KMO score of.936 is higher than the minimum requirement

(0.7), demonstrating that the dataset is sufficiently sampled to be used in a factor analysis (Woodcock Johnson, 2019). This lends credence to the study's central topic and demonstrates the reliability of the data in illuminating the complex connection between social media usage, employee performance, and organizational success. In addition, there are robust correlations between the variables, as shown by Bartlett's Test (Sig. =.000) (Bartlett, 1950). The statistical findings offer a solid groundwork for future study into the impacts of social media usage on both people and organizations, demonstrating the validity and trustworthiness of the data in answering the research question. Communalities Table 5: Communalities Initial Extraction KS 1 1.000 .695 KS 2 1.000 .820 KS 3 1.000 .777 KS 4 1.000 .624 KS 5 1.000 .789 KS 6 1.000 .780 KS 7 1.000 .735 COMM 1 1.000 .758 COMM 2 1.000 .803 COMM 3 1.000 .802 COMM 4 1.000 .717 COMM 5 1.000 .796 COMM 6 1.000 .820 COMM 7 1.000 .785 COMM 8 1.000 .830 COMM 9 1.000 .858 COMM 10 1.000 .856 COMM 11 1.000 .806 COMM 12 1.000 .795 DM 1 1.000 .628 DM 2 1.000 .713 DM 3 1.000 .675 DM 4 1.000 .705 EP 1 1.000 .760

EP 2 1.000 .802 EP 3 1.000 .807 EP 4 1.000 .827 EP 5 1.000 .833 EP 6 1.000 .804 OP 1 1.000 .799 OP 2 1.000 .721 OP 3 1.000 .734 OP 4 1.000 .753 OP 5 1.000 .721 Extraction Method: Principal Component Analysis. Validity may be evaluated in part by determining how much overlap there is between the observed variables and the extracted components using Principal Component Analysis (PCA). At the outset, all communalities were at a perfect 1.000, indicating that every possible type of shared variation was captured by the measured variables. The extraction procedure, however, accounted for some previously unexplained shared variation, which led to diminished communalities (Boyd et al., 2007). The extraction communalities vary from 624 to 858, thus even if the degree of reduction varies between products, most still retain a sizable percentage of their variance. The results imply that the generated factors successfully account for a considerable percentage of the variance in these variables. These results add to the body of evidence supporting the reliability and validity of the measurement instruments by showing that there is a continued high degree of correlation between the variance in the items measuring social media use, communication, decision making, employee performance, and organizational performance and the corresponding constructs. The numbers are also consistent with the recommended criterion for communalities, which is 0.6 or higher (Dhir et al., 2018), demonstrating the validity of the investigation. Total Variance Explained Table 6: Total variance explained

Compone nt Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Varianc e Cumulati ve % Total % of Varianc e Cumulati ve % Tota l % of Varianc e Cumulati ve % 1 16.1 52 47.505 47.505 16.15 2 47.505 47.505 9.61 9 28.292 28.292 2 6.10 2 17.948 65.453 6.102 17.948 65.453 6.47 0 19.030 47.322 3 1.45 4 4.278 69.731 1.454 4.278 69.731 4.84 2 14.241 61.562 4 1.30 1 3.826 73.557 1.301 3.826 73.557 3.80 9 11.203 72.765 5 1.11 8 3.289 76.846 1.118 3.289 76.846 1.38 7 4.081 76.846 6 .789 2.322 79.168 7 .763 2.244 81.412 8 .545 1.604 83.016 9 .501 1.473 84.489 1 0 .455 1.338 85.827 1 1 .415 1.222 87.049 1 2 .381 1.121 88.171 1 3 .361 1.063 89.234 1 4 .335 .984 90.218 1 5 .307 .902 91.120 1 6 .295 .868 91.988 1 7 .272 .801 92.789

1 8 .248 .730 93.519 1 9 .234 .687 94.205 2 0 .207 .610 94.815 2 1 .206 .607 95.422 2 2 .177 .522 95.944 2 3 .174 .510 96.454 2 4 .153 .450 96.904 2 5 .143 .419 97.324 2 6 .137 .403 97.726 2 7 .127 .374 98.101 2 8 .117 .343 98.444 2 9 .109 .321 98.765 3 0 .103 .303 99.068 3 1 .091 .268 99.336 3 2 .081 .238 99.575 3 3 .076 .223 99.798 3 4 .069 .202 100.000 Extraction Method: Principal Component Analysis.

Principal Component Analysis (PCA) gives significant insights into the dimensionality and structure of the data about the impact of social media use on employee and organizational performance by analyzing the total variance explained. The amount of variation that may be attributed to the extracted components is highlighted by their respective initial eigenvalues. Specifically, the first main component explains 47.505% of the total variation, whereas succeeding components explain progressively less variance. The sixth component alone accounts for over 76% of the remaining variation which is above the accepted value (70%) (Dhir et al., 2018). The results of this analysis indicate that the extracted components, especially the first few, account for a substantial proportion of the observed variation in the data. This shows that the factors extracted during PCA can accurately represent the data's underlying structure and relationships, lending credence to the reliability of the metrics and laying a solid groundwork for further research into the impact of social media on productivity at work. Rotated Component Matrix (EFA) Table 7: Rotated Component Matrix Compone nt COMM DM EP KS OP COMM 1 0.670 COMM 10 0.797 COMM 11 0.795 COMM 12 0.787 COMM 2 0.725 COMM 3 0.753 COMM 4 0.652 COMM 5 0.601 COMM 6 0.608

COMM 7 0.740 COMM 8 0.812 COMM 9 0.822 DM 1 0.679 DM 2 0.785 DM 3 0.764 DM 4 0.792 EP 1 0.782 EP 2 0.768 EP 3 0.787 EP 4 0.747 EP 5 0.774 EP 6 0.744 KS 1 0.739 KS 2 0.784 KS 3 0.752 KS 4 0.581 KS 5 0.658 KS 6 0.661 KS 7 0.639 OP 1 0.798 OP 2 0.828 OP 3 0.800 OP 4 0.814 OP 5 0.787 AVERAG E 0.730 0.755 0.767 0.688 0.805 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 9 iterations. The relationships between the extracted components and the observable variables associated with social media use, communication, decision making, employee performance, knowledge sharing, and organizational performance can be better understood by examining the rotated component matrix derived from Principal Component Analysis (PCA) with Varimax rotation and Kaiser normalization. After rotating the data, the underlying structure is more clear, and it is obvious that numerous items stress heavily on their respective components (Boyd et al., 2007). COMM 8 and COMM 9 are two examples that stand out because of the heavy weightings they have on the communication aspect. Equally important

in the decision-making process are DM2 and DM4, both of which have high loadings on the decision-making subcomponent. The relevance of EP 1, EP 2, and EP 3 in the context of employee performance is highlighted by the presence of considerable loadings on these dimensions. The components that make up the knowledge-sharing and organizational- performance variables also have substantial loadings. This component rotation strengthens the basis for investigating how social media use affects employee and organizational performance by enhancing the construct validity of the assessment items by exposing coherent patterns of connections across variables. It is important to keep in mind that the minimum allowable value for factor loadings (the loadings of items on their respective components) varies with the nature of the data being analyzed and the desired outcomes. Loadings on factors over 0.7 are generally regarded as excellent, while loadings above 0.5 are regarded as adequate (Dhir et al., 2018). Additionally confirming the construct validity of the measuring items in this research are high average factor loadings for the components in this rotated matrix (AVERAGE). Discriminant Validity Table 8: Discriminant validity table KS COMM DM EP OP KS Pearson Correlation 0.688 Sig. (2-tailed) N 206 COMM Pearson Correlation .822 ** 0.730 Sig. (2-tailed) .000 N 206 206 DM Pearson Correlation .406 ** .458 ** 0.755 Sig. (2-tailed) .000 .000 N 206 206 206 EP Pearson Correlation .340 ** .404 ** .768 ** 0.767 Sig. (2-tailed) .000 .000 .000 N 206 206 206 206 OP Pearson Correlation .371 ** .385 ** .789 ** .820 ** 0.805 Sig. (2-tailed) .000 .000 .000 .000 N 206 206 206 206 206 **. Correlation is significant at the 0.01 level (2-tailed).

All of the correlation coefficients in our discriminant validity study are significant at the two-tailed 0.01 level, demonstrating strong links between the various components. Given that within-construct correlations are often higher than cross-construct correlations, these results lend credence to the concept of discriminant validity. The association between knowledge structure (KS) and communication mode (COMM) is 0.688, which is positive but less than the correlations between other KS and COMM subscales. It is clear that the constructs in this research measure different qualities since the correlations between KS and the other constructs (DM, EP, OP), and between COMM and the other constructs, are all below 0.7. These findings provide strong support for the discriminant validity of the measuring items used to assess social media usage, communication, decision making, and employee/organizational outcomes. These results, which conform to the HTMT criteria for discriminant validity, are essential for establishing the reliability of future research evaluating the impact of social media use on the productivity of both employees and businesses. 4.5 Descriptive statistics Table 9: Descriptive statistics N Mean Std. Deviation Skewness Kurtosis Statist ic Statistic Std. Error Statistic Statist ic Std. Erro r Statist ic Std. Erro r KS 206 3.785714285714 287 . 0568490637660 04 . 8159375628812 89 -.619 .169 .496 .337 COM M 206 3.854368932038 835 . 0558478026532 87 . 8015667624142 79 -.761 .169 .993 .337 DM 206 3.1650 .05732 .82274 .099 .169 .175 .337

EP 206 3.269417475728 156 . 0605765957974 89 . 8694377122214 95 .240 .169 -.314 .337 OP 206 3.162135922330 096 . 0585370346298 63 . 8401645024583 56 .301 .169 -.205 .337 Valid N (listwis e) 206 The table's numbers provide light on how social media affects productivity in the workplace. There is a small but significant positive trend in the responses (KS mean = 3.79, COMM mean = 3.85) about the overall thoughts of the 206 respondents towards social media. When asked about DM, however, respondents' attitudes are more evenly distributed (mean = 3.17, skewness around zero), showing that people are not too concerned about the influence of social media on their ability to make decisions. Even though the skewness values suggest a modest positive skew, the data demonstrate that both employee performance (EP) and organizational performance (OP) are generally seen favorably (EP mean = 3.27, OP mean = 3.16). For a normal distribution to be assumed, the skewness value should be between -2 and 2, and the kurtosis value should be between -7 and 7. The skewness values in this data set are as follows: KS = 0.816, COMM = 0.801, DM = 0.822, EP = 0.869, and OP = 0.840. These skewness scores are within the typical tolerance range, suggesting that the data may have a normal distribution. Also within the allowed range for kurtosis (-2.0 to 2.0) are the values of 0.496 for KS, 0.993 for COMM, 0.175 for DM, -0.314 for EP, and -0.205 for OP. These findings point to the possibility that the dataset has a distribution quite close to the normal one. The Shapiro-Wilk test and the Kolmogorov-Smirnov test are two further statistical analyses that may be performed to verify the normality of the data. More research

is required to fully appreciate the complex relationships between social media use and business outcomes. 4.6 Correlation Table 10: Correlation KS COMM DM EP OP KS Pearson Correlation 1 Sig. (2-tailed) N 206 COMM Pearson Correlation .822 ** 1 Sig. (2-tailed) .000 N 206 206 DM Pearson Correlation .406 ** .458 ** 1 Sig. (2-tailed) .000 .000 N 206 206 206 EP Pearson Correlation .340 ** .404 ** .768 ** 1 Sig. (2-tailed) .000 .000 .000 N 206 206 206 206 OP Pearson Correlation .371 ** .385 ** .789 ** .820 ** 1 Sig. (2-tailed) .000 .000 .000 .000 N 206 206 206 206 206 **. Correlation is significant at the 0.01 level (2-tailed). The correlation research sheds light on the connections between employees' social media habits and their overall productivity. N = 206 people filled out the survey and participated in the research. First, there is a substantial positive connection between KS and COMM (r = 0.822, p 0.01), suggesting that those individuals who reported greater KS consumption also likely to participate in more COMM activities. The implications for the function of social media in promoting dialogue at work are intriguing. Second, social media use (KS; r = 0.406, p 0.01) and communication (COMM; r = 0.458, p 0.01) are both positively connected with Decision Making (DM). Some of the respondents, then, who use and communicate more often through social media are also likely to engage in greater Decision Making. This could make us wonder how the ability to make decision influences productivity in the workplace. Employee Performance (EP) is positively correlated with the following behaviors: social media use (KS; r = 0.340, p 0.01), communication (COMM; r = 0.404, p 0.01), and Decision Making (DM; r = 0.768, p 0.01).

This might have repercussions for productivity in the workplace, since regular social media users are also likely to be conversant in a variety of communication channels and adept at juggling many Decision Making tasks at once. Finally, knowledge sharing (KS) (r = 0.371, p 0.01), communication (COMM) (r = 0.385, p 0.01), Decision Making (DM) (r = 0.789, p 0.01), and Employee Performance (EP) (r = 0.820, p 0.01) all have favorable relationships with organizational performance (OP). These results suggest that increased social media use, communication, Decision Making, and Employee Performance are all linked to better organizational performance for certain respondents. This indicates that social media use may influence employee and business results in a variety of ways. 4.7 Regression Table 11: Dependent variable: EP Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Durbin- Watson R Square Change F Change df1 df2 Sig. F Change 1 .771 a .594 .588 .557847587207895 .594 98.655 3 202 .000 2.039 a. Predictors: (Constant), DM, KS, COMM b. Dependent Variable: EP The regression model is highly significant (F = 98.655, p 0.001) and accounts for a considerable amount of variation in worker performance (R2 = 0.594). Use of social media, DM, and COMM are the factors in the model that account for approximately 60% of the variance in employee performance. This indicates that workers' performance is strongly affected by their usage of social media and their involvement in decision-making and communication. Overfitting the data is avoided since the model is stable (R2 = 0.588 after adjustments; Walther, 1992). Significant indicators of employee performance (p 0.001) include the usage of social media for knowledge sharing (KS), decision making (DM), and

interpersonal communication (COMM). More time spent on social media is correlated with increased productivity, as shown by a positive regression coefficient. Furthermore, the positive associations for both DM and COMM show that participation in these activities is related to enhanced performance on the job. Statistically, this regression model fits the data well, and the insights it gives on the correlation between social media usage and productivity in the workplace are helpful. Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics B Std. Error Beta Zero- order Partial Part Tolerance VIF 1 (Constant) .548 .209 2.626 .009 KS -.045 .084 -.042 -.531 .596 .340 -.037 -.02 4 .323 3.093 COMM .108 .088 .099 1.225 .222 .404 .086 .055 .306 3.269 DM .782 .053 .740 14.650 .000 .768 .718 .656 .787 1.270 a. Dependent Variable: EP Insight into the associations between the predictors and EP is provided by the model's coefficients. The constant term is 0.548 (p = 0.009), demonstrating that there is still some baseline emotional awareness when all predictors are adjusted to zero. A minor standardized coefficient of -0.042 and a negative unstandardized coefficient of -0.045 suggest that knowledge sharing (KS) is a poor predictor. Social media usage for knowledge sharing does not seem to have a strong linear relationship with employee performance (p=0.596), according to the data. Similarly, the standardized coefficient for communication (COMM) is 0.099 and the p-value for this relationship to employee performance is 0.222. Decision Making (DM), on the other hand, has a big positive unstandardized coefficient of 0.782 and a very significant positive standardized coefficient of 0.740 (p 0.001), indicating that it has a major and positive effect on worker performance. These results pass the significance test (p > 0.05). Thus, among the predictors included in this model, Decision Making is the most consequential in terms of its effect on worker output.

The predictive model Assuming that the value of x=100, we can predict employee performance as follows Y= C+β(x)+β(x) Y=Dependent Variable C= Constant value β= Beta value of variables x = Independent Variable Y = 0.548 - 0.045(100) + 0.108(100) + 0.782(100) Y = 0.548 - 4.5 + 10.8 + 78.2 Y = 84.148 So, with x = 100, the predicted employee performance (EP) is approximately 84.148. This model shows how different factors (KS, COMM, and DM) impact employee performance Dependent variable: OP Model Summary b M odel R R Squar e A djusted R Square Std. Error of the Estimate Change Statistics D urbin- Watson R Squ are Cha nge F Cha nge d f1 d f2 S ig. F Chang e 1 . 791 a . 626 . 620 . 517854979 098818 .626 112. 531 3 2 02 . 000 1 .973 a. Predictors: (Constant), DM, KS, COMM b. Dependent Variable: OP According to the data, these factors explain 62.6% of the variation in organizational performance. With an adjusted R 2 of 0.620, the model fits the data quite well. The model's

statistical significance (p 0.001) demonstrates the importance of these factors in elucidating business effectiveness. Use of KS, DM, and COMM as predictors of organizational success all contribute substantially (p 0.001), underscoring their weight. These results show how taking part in social media activities may boost an organization's overall efficiency. Coefficients b Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics B Std. Error Beta Zero- order Partial Part Tolerance VIF 1 (Constant) .485 .194 2.503 .013 KS .102 .078 .099 1.306 .193 .371 .091 .056 .323 3.093 COMM -.052 .082 -.050 -.640 .523 .385 -.045 -.02 8 .306 3.269 DM .788 .050 .771 15.895 .000 .789 .745 .684 .787 1.270 a. Dependent Variable: OP According to the model, these aspects account for a significant portion (62.6% or more) of the variation in organizational performance. The predictors have a significant impact, but the constant term is just 0.485 (p = 0.013), suggesting a minimum acceptable level of performance. More specifically, a significant positive impact ( = 0.771, p 0.001) is shown for Decision Making (DM), indicating that certain respondents who participate in DM more often also likely to have better organizational performance. Decision Making has a distinct impact on organizational performance, while using social media in sharing knowledge and communicating (COMM) do not exhibit significant impacts. The predictive model Y= C+β(x)+β(x) Y=Dependent Variable C= Constant value β= Beta value of variables x = Independent Variable

Only if the results are statistically significant may the variables' beta values be calculated. A p-value of less than 0.05 is considered statistically significant. Assuming that Maldives Police Service spend 100$ EP= β(TRS)+ β(LF) + c EP = -0.50 (100) + 0.771(100) + 0.099 EP = -70 + 77.1 + 0.099 EP = 27.199 Because the constant value and TF are not statistically significant, beta values were used to calculate TRS and LF. Therefore, this estimate suggests that a 27.199 percent boost in employee performance is possible if Maldives Police Service places a greater emphasis on transactional leadership and laissez-faire leadership. 5.0 Finding and Discussion Insightful conclusions may be drawn from the data analysis on the impact of social media use on the efficiency of the Maldives Police Service and its personnel. In terms of age distribution and gender distribution, the majority of respondents were male. Various demographic groups may use social media in various ways, which might have an effect on performance that varies according to the group (Kayumovich, 2019). The diversity of respondents' job titles and fields of expertise also suggests that these factors may have an impact on whether or not they utilize social media for business. When it comes to choosing the right statistical procedures, knowing that certain variables do not follow a normal distribution is vital. When investigating the connection between social media use and productivity, researchers should think about using alternative statistical methods that do not depend on the assumption of normalcy (Huang et al., 2017). Measures of social media participation, employee performance, and organizational results were all evaluated with high reliability, suggesting that the methods used to make these assessments may be trusted.

The determinant value, KMO, and Bartlett's Test, all components of the validity analysis, gave strong evidence that the data were appropriate for factor analysis, lending credence to the validity and reliability of the research as a whole. In order to examine the effects of social media usage on employee and organizational performance, a reliable foundation must be established, and discriminant validity analysis provided just that (Hee, 2020). Social media usage, communication, Decision Making, Employee Performance, and organizational performance all showed significant correlations and regressions. Specifically, research revealed that Decision Making had a very beneficial effect on both individuals' Employee Performance and the efficiency of their organizations (Fakhr et al., 2019). Employees who spend more time Decision Making may benefit from enhanced Employee Performance and enhance the organization's performance. Taken together, these results provide a solid Launchpad for further research into the intricate web of connections between the Maldives Police Service's social media presence and its efficiency and effectiveness. 6.0 Conclusion, Implications and Recommendation 6.1. Conclusion In conclusion, the data analysis done for this study has shed light on how social media affects the productivity of police officers in the Maldives. Gender, age, marital status, and occupation all had a role in respondents' social media habits, highlighting the necessity for a comprehensive knowledge of these characteristics when assessing social media's effect on productivity. Further, the data normality, reliability, and validity analysis validated the study's rigorous research methodology, providing further evidence that the study's measurement tools accurately captured various facets of social media participation, communication, decision making, and performance outcomes. The complicated linkages between social media usage, Decision Making, communication, and Employee Performance, and their effects on employee and

organizational performance are further illuminated by correlation and regression analysis. According to the results, Decision Making improves both Employee Performance and organizational effectiveness significantly. However, neither the frequency nor the content of social media interactions were shown to be significant predictors of individual or group Employee Performance or organizational performance. In light of these findings, it is clear that companies need to take into account the roles and impacts of different Decision Making activities when creating rules and strategies for employees' usage of social media at work. In sum, this study aids in expanding our knowledge of social media's function inside the Maldives Police Service and laying the groundwork for future inquiries and well-informed policy and practice decisions. 6.2 Implications The work has important theoretical and applied ramifications, and the results are many. First, the study's heterogeneous sample—which included participants of different sexes, ages, marital statuses, and professional backgrounds—highlights the possible moderating impacts of these variables on social media usage in the workplace (Dhir et al., 2018). When developing firm rules and plans for social media usage, it is crucial to recognize that the influence of social media on productivity and performance may vary depending on individual characteristics. Furthermore, the research highlights the fact that many participants utilize social media for professional reasons, suggesting that professionals in the Maldives acknowledge the value of social media in their job. The results of this study suggest that businesses should strategically include social media into processes such as internal communication, information exchange, and decision making. Providing workers with training and standards is vital to maximizing the beneficial influence of social media in the workplace. Third, the study's studies of reliability and validity show that the measuring instruments employed to evaluate social media participation, worker output, and business

results are accurate and reliable predictors of the variables of interest. Researchers and practitioners may now feel secure utilizing these tools to investigate the impact of social media on workplace productivity. The study provides a theoretical framework for the need of doing more correlation and regression research into topics including social media use, communication, decision making, employee performance, and organizational performance. These results highlight the substantial beneficial influence of decision making on individual and organizational performance, implying that fostering better decision-making abilities among workers may increase firms' output. 6.3 Recommendations Given that people of all ages and backgrounds use social media in different ways, it is essential for institutions like the Maldives Police Service to provide individualized training courses. The technical aspects of social media use are important, but so is teaching participants how to use them responsibly and effectively. Various departments and employees of varying ages may have various training requirements and learning styles. Knowledge sharing (KS) and Employee Performance (EP) are positively correlated, suggesting that encouraging KS in the workplace via social media may improve both KS and EP. Businesses should encourage their staff to use social media to share their expertise with one another, since this will boost employee engagement and productivity. Since Decision Making (DM) is positively correlated with both Employee Performance (EP) and organizational performance (OP), it is crucial to identify and control DM habits. Organizations should give staff with recommendations for efficient time management and job prioritization, as well as training on the advantages and disadvantages of Decision Making process. Strategic and efficient communication via social media platforms is crucial because of the crucial role that communication (COMM) plays in the Employee Performance (EP) of

employees and the performance (OP) of organizations. To achieve their goals, businesses should implement effective communication strategies and provide staff with the necessary training to use social media in a professional manner. Organizations should create regular evaluations and feedback systems to track the effects of social media use on productivity. The sooner trends, improvement opportunities, and unintended repercussions are uncovered, the sooner social media policies and procedures may be adjusted to mitigate their effects. Leaders in organizations should reflect on their own leadership styles and make necessary adjustments to maximize staff productivity. Particular styles of leadership, such as transactional and laissez-faire, should be utilized sparingly and with an awareness of how they affect worker productivity. Since officers in the Maldives Police Service use social media for official business, protecting their personal information is a top priority. Strong cybersecurity safeguards should be implemented, and staff should be educated on the necessity of being cautious online. 6.4 Limitations and Suggestions for Future Research The data depends on self-reported information, which may be impacted by social desirability or erroneous recall, both of which may introduce response bias into the research. Furthermore, the cross-sectional nature of the research makes it difficult to draw any firm conclusions about a cause-and-effect relationship between social media use and productivity in the workplace. The future course of the connection may be better determined using longitudinal data. In addition, the research was conducted inside a unique organizational setting (the Maldives Police Service), which may restrict the transferability of the results to other sectors or geographical areas. The effects of social media usage in the workplace may vary depending on factors such as the company's social media policy and the company culture. Finally, although the research did look at different components of social media usage

and how they related to performance, it did not investigate the processes behind these associations or any possible moderating variables. More in-depth qualitative studies of social media use within the Maldives Police Service and other organizations are proposed for future study. Further insights might be gained by investigating how organizational policies and leadership styles influence the impact of social media on performance. Stronger causal conclusions might be made from research that follows social media use and performance over time. In addition, the research's generalizability might be improved by expanding into more sectors and geographical areas. Finally, future study should concentrate on exploring the possible drawbacks and negative repercussions of excessive social media usage in the workplace to give a more nuanced view of this multifaceted phenomena.

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