Conventional Mass Media and Online newspapers
The technology and mass media are combined to share the information. There are more mass medias such as television, radio and newspaper organizations which transmit information electronically as well as printed form (Hettiarachchi, 2017). Wireless communication is the transfer of information from any place without cables. The data transmissions without cables are widely used in every field and the one of principal technologies involved in wireless communications is radio frequency (Arimany, 2011). According to Arimany (2011) radios are using wireless communication of signals electromagnetic radiation of a frequency significantly below that of visible light, in the radio frequency range, from a few Hz to 300 GHz. These waves are called radio waves. Electromagnetic radiation travels by means of oscillating electromagnetic fields that pass through the air and the vacuum of space.
Wijayaratne and Marikar (2012) have shown that television is a telecommunication medium typically used in the 2000 for transmitting and receiving moving color metaphors and sound. In the broader sense, television can also refer to images that can be black and white or with or without associated sound. Television has usually offered general materials for conversation and several function of “window to the world” for a fairly sedentary public (Summa, 2011). late 1920 the television has become commercially available and become common place in homes, businesses and every kind of institutions, particularly as in the vehicle for advertise, source of entertainments, and news. Since the 1950, television has been the main medium for molding public opinion. Since the mid-1960, color televisions have been widely available (Deacon, 2013).
Newspapers were become a common mass media in the 16th century. The newspaper industry has been around in 4 centuries. Perera (2018) noted that the newspaper is an invention of Europe. The first printed newspaper was published in 1609 in Germany. Since then, newspaper industry has been gradually developed. However, Perera (2018) further mentioned that newspaper industry was impacted due to the invention of radio in 1930s, then from television from 1950s. Afternoon papers were critically hindered with television. Further, with the growth of internet after 2000 and with sophisticated mobile phones after 2010, newspaper industry faced many challenges. Printed newspaper circulation has been fell down in Sri Lanka and other many countries over the past decades due to the higher utilization of online media and cable news (Central Bank of Sri Lanka, 2019).
Consequently, online newspapers have been widely used, at least since the early 1990, to refer to communication media on the world-wide web (WWW), which is so over spring an internet application that it is often taken almost synonymously as the internet (Nguyen, 2006). Reading online news represents to the different experience for online newspapers users since the users have an active role in their relations with news industry. Previous generations mainly read the news in form used of printed newspapers, most people are today, with the rise of internet, complementing to their research for the latest news stories by reading news online. It creates a trend called “online newspaper” (Sünnen, 2012). Online newspapers are always like hard-copy newspapers not only that online newspaper has same legal boundaries, such as laws regarding libel, privacy and copyright, also apply to online publications in most countries (Schoneville, 2007). Patel (2010) argued that internet began to take shape as the ‘World Wide Web’, online newspaper has begun to circulate news. Ever since, the impact it has had on newspapers is twofold. Directly, the internet has increased competition to the newspaper industry and revised the way news is distributed and indirectly, the internet has influenced advertising trends and consumer behavior. According to the Nnagbo (2011), on the online newspaper, the users have the chance to interact with the news provider, immediately sending comments on articles.
Traditionally, people are reading printed newspapers to get to know the current happening in the country and as well as around the world. The internet provides lot of services such as watching television, making phone calls, looking up information or reading online news (Schoneville, 2007). Universities and schools tend to offer more online classes during this COVID-19 pandemic, meantime news corporations provide online newspapers and publishers release more online books and journals. As a result, the amount of text based information available via online is steadily increasing. People use the internet to seek information, read news, to communicate, and for entertainment purposes (Vermeer, Trilling, Kruikemeier, and Vreese, 2020). With this improvement more people have been encouraged to use internet further more.
According to Sequeira (2014) conceptualization is breaking and converting research ideas into common meanings to develop an agreement among other users. This study was mainly focused on finding the factors to adaption of online newspaper reading with COVID-19 pandemic among the staff members of the Rajarata University of Sri Lanka. Researcher found some influencing factors which affect to the adaption of online newspaper reading. (Schoneville, 2007; Hettiarchchi, 2017; Kumar, 2018; Lu & Zhang, 2018; Zukowski & Brown, 2014).
Social Influence:
Social influence is defined as the degree to which an individual perceives that important others believe he or she should use the new (Venkatesh, Morris, Davis and Davis, 2003). According to Zukowski and Brown (2014), social influence is the degree to which the individual confide that significant other confide that they should use the technology (Zukowski & Brown, 2007). Social influence is occured in online spaces, and is supported in to the range of devices such as smart phones, computers, and tablets (Kim & Hollingshead, 2016). According to Hettiarchchi (2017), however social influence can be an important motivation for adopting of new technologies. Early research, for example investigating the role of social influence in the field of e-services, has found a direct effect on adaption (Hettiarachchi, 2017). Social influence has been supported to the customer for adaption online services (White, 2009). According to Schoneville (2007), social influence is related with behavioral intention of use online newspaper reading and Schoneville said social influence significantly affect online newspaper reading behavior. Yang (2011) also noted that social influence significantly affects online reading specially news in blogs. Paying attention into above evidences, researcher was motivated to present following hypothesis;
H1 :There is a significant relationship between social influence and adoption to online newspaper reading with the COVID-19 pandemic among the staff members of Rajarata University of Sri Lanka.
Online Habit:
Different people have different kind of habits. According to Schoneville (2007), habit is the extent to which using online newspaper has become automatic in response to certain situations. According to Schoneville (2007), habit was significantly affected online news reading behavior. In addition, Heather (2011) mentioned a short definition which is particular practice, custom, or usage in habit or experience may be converted to the habit. According to Kumar (2018), habit mainly affects to using e-services example e-book, e-newspapers. Accordingly, individuals’ habit mainly affects online services more than the time before the COVID-19 pandemic. Krishnamurthy and Awari (2015) conducted a study on newspaper reading habit among post graduate students of Karnataka University. Krishnamurthy and Awari noted that the habit make influences on online newspaper reading. Yadamsuren and Erdelez (2011) also has empazised most of the time, online reading is materialized on individual’s habitual basis. Accordingly, above noted arguments were supported to researcher to develop a hypothesis as mentioned below;
H2 : There is a significant relationship between online habit and adoption to online newspaper reading with the COVID-19 pandemic among the staff members of Rajarata University of Sri Lanka.
Online Experience:
Flavian and Gurrea (2007) concluded that internet experience influences on the relationship between the reader goal and reading in the digital medium. The study conducted by Lu and Zhang (2018) revealed that user online experience mainly affects online reading behavior. All of the electronic services some time depend on user online experience. So that, usage of online services had been increased due to the online experience already users have. Furthermore, it has been highlighted that the internet experience has a significant effect on consumers’ choices and final preference (Flavián & Gurrea, 2006). Same argument can be found in the study done by Beyers (2018). According to his findings, online experience of users may be mainly affected on reading materials which are available in the internet. Hence it implies that users those who have less online experience, they could not adopt easily towards the online environment. According to Constantinides (2014), it was found that experience of suffering web has been stimulated the adaption to online customer behavior. Subsequently, researcher was encouraged to have below noted hypothesis for the study;
H3 <: There is a significant relationship between Internet experience and adoption of online newspaper reading with the COVID-19 pandemic among the staff members of Rajarata University of Sri Lanka.
Demographic Factors and online reading
Age, gender, education and income have been taken as demographic factors in most of the studies (Nguyen, 2006). Finding of his study reveals that age, education and income moderately affect online news reading. In addition, being a male, being a professional, being in the labor force and living in a metropolitan area users play various roles and can be observed different behavior for online platform. According to Stoneville (2007), age, gender and experience moderatly affect towards the reading online newspaers. Well known and cited model of UTAUT shows the moderating effect of age on social infuence (Venkatesh, Morris, Davis and Davis, 2003). Females and males differ in their attitudes toward access online services. Females are often more concerned about the control of personal information and they could not using online services (Zukowski & Brown, 2007). Individuals with lower levels of education may perform less online services because they haven’t knowledge to use e-services. According to the study of Spyridou and Veglis (2008) emphasized that among demographics one was partially confirmed as education was found to be the only variable influencing online news consumption, demonstrating that the higher the level of education, the more likely students are to resort to the web for news retrieval. Schoneville (2007) studies suggest that demographic factors of age moderately affect social influence and habit. Different age limits have different newspaper reading choice. Wijayaratne and Marikar (2012) revealed 15 and 30 age limit readers are mostly use online news paper but 50 years above age limit readers can not accept online newspaper. Considering literature evidences noted above, gender, age and education are treated as moderating variables on the relationships towards adoption to online newspaper reading.
Accordingly, social influence, online habit, internet experience are treated as independent variables and demographic factors is the moderating variable. Adaption to online newspaper reading is the dependent variable. The relationships between dependent variable and independent variables are drawn on the following research model.
Methodology and Analysis
Quantitative research method was applied for the study. Study basically explored the factors affect for adaption to online newspaper reading with COVID-19 pandemic. Explanatory variables were measured numerically by fulfilling the basic criteria of quantitative study. Individual respondent carries the role of unit of analysis. The quantitative methods helped in generating numerical data, which was statistically manipulated to meet required objectives through descriptive statistics, inferential statistics and hypotheses were tested by correlation analysis (Amin, 2005). Multiple regression analysis was carried out to identify the importance of the predictors on dependent variable. All statistical techniques were executed with the SPSS software.
Staff members (academic and non-academic executive) of all six faculties of the Rajarata University of Sri Lanka were selected for the study. Hence, sample was equal population. Standardized structured questionnaire was used to collect data and questionnaires were distributed as follows.
Table 1
Population and Sample distribution
Faculties of Rajarata University | Academic Permanent Staff Members | Non-Academic Executive Staff Members | Total |
Faculty of Management Studies | 47 | 1 | 48 |
Faculty of Medical and Allied Science | 54 | 2 | 56 |
Faculty of Applied Science | 40 | 2 | 42 |
Faculty of Technology | 20 | 1 | 21 |
Faculty of Agriculture | 44 | 2 | 46 |
Faculty of Social Science and Humanity | 55 | 1 | 56 |
Non-Academic Staff members (Admin) | - | 22 | 22 |
Total Staff Members | 260 | 31 | 291 |
Source: official records, Rajarata University of Sri Lanka |
Validity is the state or quality of being valid (Sekaran, 2006; Vogt, 2007). Validity was checked to ensure the quality of the instrument. Face validity and content validity were ensured by the expert in the field. Internal consistency can be checked by Cronbach’s coefficient alpha (Sekaran, 2006). According to Shuttleworth (2015), reliability is the degree of consistency of a measure and a test will be reliable when it gives the same repeated result under the same conditions. Taherdoost (2016) says that there is no any constant value for internal consistency. However the minimum internal consistency coefficient is 0.7. According to Straub, Boudereau and Gefen (2004) reliability should be equal or above 0.6 to proceed the analysis.
Table 2
Variable | No. of item | Cronbrach’s alpha |
Online Habit | 6 | .929 |
Social Influence | 6 | .908 |
Internet Experience | 5 | .868 |
Adaption of Online Newspaper Reading | 6 | .933 |
Cronbrach’s alpha value of all variables are ranged from 0.868 to 0.933. Generally, values should be located in between 0.7–1.0. If the alpha value lies between that range, it concludes that all variables have high reliability ensuring the internal consistency.
Test the moderating effect of demographic factors
Three demographic factors (gender, age and education level) were considered to check moderating impact to relationships of independent variables to dependent variable. Andrew F. Hayes moderating variable analysis was applied to check the moderating impact. Moderating variable effect is determined with significant (p) value and it should always less than 0.0005 (p < 0.0005) and R Square Change value represent how much moderating variable effect the relationship between independent and dependant variable (Aguinis, 2004; Jaccard & Turrisi, 2003; Jose, 2013).
Table 3
Summary values of Andrew F. Hayes moderating variable analysis
| | R2- Chng | F | df1 | df2 | P |
Social Influence | X*W | .0003 | .2786 | 1.0000 | 156.0000 | .5983 |
Internet experience | X*W | 0.061 | 18.0180 | 1.0000 | 156.0000 | 0.0000 |
Online habit | X*W | 0.1070 | 20.7434 | 1.0000 | 156.0000 | 0.0001 |
The values shown in the Table 3 revealed only the relationships of internet experience and online habit with adoption to online newspaper reading are moderated. There is no moderating impact on the relationship between social influence and the dependent variable. Highest moderating effect (10.7 percent) is reported online habit and adoption to online newspaper reading.
Hypotheses Testing
Correlation analysis explains the relationship between variables (Sekaran, 2006). Hence, Pearson’s correlation coefficient was computed to determine the relationship between dependent variable and independent variables. It indicates the strength and the direction of the relationship. The sign of the Correlation coefficient shows the direction of the relationship between − 1.00 and + 1.00. Variables may be positively or negatively correlated. Thus, values of the below Table 4 confirmed that all relationships with adoption on online newspaper reading is positively correlated. Accordingly, all hypotheses were supported.
Table 4
Correlation coefficient values
| | A | B | C | D |
A | Pearson Correlation | 1 | .838 | .822 | .868 |
Sig. (2-tailed) | | 0.000 | 0.000 | 0.000 |
B | Pearson Correlation | .838 | 1 | .904 | .915 |
Sig. (2-tailed) | .000 | | .000 | .000 |
C | Pearson Correlation | .822 | .904 | 1 | .887 |
Sig. (2-tailed) | .000 | .000 | | .000 |
D | Pearson Correlation | .868 | .915 | .887 | 1 |
Sig. (2-tailed) | .000 | .000 | .000 | |
A - Online Habit, B - Internet Experience, C - Social Influence, D - Adaption to Online Newspaper Reading, N – 160 |
Regression analysis
Multiple regression analysis was executed to measure the impact of independent variables (Social Influence, Internet Experience, Online Habit) on the adaption of online newspaper reading. The results of analysis are summarized as shown in following Tables 5.
Table 5
Results of regression analysis
Model | B | Std. Error | Standardized Coefficients Beta | t | Significant value |
Constant | − .241 | .115 | . | -2.084 | .039 |
Online Habit | .318 | 0.058 | .290 | 5.500 | .000 |
Internet Experience | .549 | .082 | .469 | 6.658 | .000 |
Social influence | .255 | .077 | .225 | 3.328 | .001 |
R Square – 0.88, F – 382.115, P – 0.000 |
According to the results of the regression analysis, all predictors make influences positively on adoption towards online newspaper reading. Hence, it can be concluded as 88 percent is explained the adoption towards online newspaper reading by selected independent variables and the most influencing factor is internet experience readers have.