The moderating effects of information overload and academic procrastination on the information avoidance behavior among Filipino undergraduate thesis writers

Abstract

Information avoidance is a behavior that could either prevent or delay consumption of information. While information avoidance has been documented in various fields of interest, its overall dynamics in the context of library and information science remains a research blankspot. The overall intent of this paper is to develop a model that examines the moderating effect of information overload and academic procrastination on the information avoidance behavior among Filipino undergraduate thesis writers. Capitalizing on Structural Equation Modeling (SEM) design, a total of 215 Filipino undergraduate thesis writers participated in the study. A multi-aspect questionnaire was used to measure the following variables: information overload, academic procrastination and information avoidance. Descriptive and inferential statistics were used to analyze the data. Results show that when students have a positive attitude towards reading, the more likely they are to employ better reading strategies and the less likely they are to exhibit information avoidance. On the other hand, the more reading strategies are used, the lower is information avoidance. Additionally, the tendency to procrastinate has less effect on the relationship between reading strategies and information avoidance and the tendency to procrastinate and acquire excessive information has less effect on the relationship between reading attitudes and information avoidance. Implications for university settings are also discussed in this paper.

Keywords

Academic procrastination Filipino thesis writers information avoidance information overload reading attitudes reading strategies structural equation model

Introduction

By and large, information avoidance (IA) is a behavior designed to prevent or delay the acquisition of available but potentially unwanted information (Sweeny et al., 2010). As a common phenomenon (Golman et al., 2017), it occurs when individuals seem to instinctively know the answer to a question, or think they know the answer, but do not want it confirmed through a simple but active information seeking (Narayan et al., 2011), and would rather avoid potentially negative comparisons that could threaten them (Huang, 2018). Through the years, scholars have viewed information avoidance as a way to reduce anxiety (Maslow, 1963) while helping individuals cope with information and respond in an appropriate manner after postponing the acceptance of information (Loewenstein, 2007). Additionally, it has been viewed as a motivated decision to remain ignorant (Sweeny et al., 2010), and a way for people to counteract threats by forgetting information (Benabou and Tirole, 2002; Shu and Gino 2012).

Historically, the concept of information avoidance has been documented in the field of medicine. Chae (2016) defined information avoidance in the field of medicine as a form of refusal to be exposed to certain information even though the topic is personally relevant. People avoid information about medical test results (Howell and Shepperd, 2013; Melnyk and Shepperd, 2012) since it may cause unpleasant emotions or diminish pleasant emotions, force actions they would rather not undertake, or challenge beliefs (Sweeny et al., 2010). Moreover, the growth of online screening yielded concerns about privacy and the audiences’ potential to access personal health information (Ramsey et al., 2019). Lipsey and Shepperd (2019), for their part, proposed that the audience may use the information to harm them, if made public. In recent years, the phenomenon of information avoidance has been a research interest in fields such as economics. For instance, Golman et al. (2017) introduced the five methods of information avoidance, namely: (a) physical avoidance, (b) inattention, (c) biased interpretation of information, (d) forgetting, and (e) self-handicapping. In behavioral medicine, people sometimes avoid learning information if they believe that the information would require them to engage in an undesired behavior that is “difficult, inconvenient, demanding, expensive, or unpleasant (Howell and Shepperd, 2012). In psychology, Sweeny and Miller (2012) mentioned that expectations about the content of unknown information (i.e. people’s best guess about information under conditions of near ignorance) should be a strong predictor of information avoidance. In marketing, Huang (2018) wrote a paper on social information avoidance where he proposed that this type of avoidance is most likely to occur when people are halfway through their pursuit and feel the least motivated to exert effort.

Notably, the emergence of the big data phenomenon has in fact posed a number of challenges to modern society. Gupta and Rani (2018) averred that big data with its key characteristics of volume, velocity, variety, variability, veracity, value, and validity vis-à-vis the heterogeneity of these sources may challenge research in terms of data acquisition, quality, storage, sharing, transfer, analysis, query and indexing, uncertainty, privacy, security and ethics, and visualization. Today, people tend to avoid information as a result of a multitude of factors which may include selective exposure (Sweeny et al., 2010), autonomy concerns (Howell and Shepperd, 2012), emotional consequences (Sweeny et al., 2010), financial decisions (Karlsson et al., 2009; Sicherman et al., 2016), privacy concerns (Truven Health Analytics, 2018), personal reasons (Sweeny et al., 2010), potential benefits and cost (Sweeny and Miller, 2012), health and responsibility (Addison, 2017), information overload (Addison, 2017), and procrastination (Ferrari and Tice, 2000), among others.

This paper argues that in the university setting, investigating the dynamics of thesis writing in the context of information overload, and academic procrastination may shed light on the information avoidance phenomenon. Information overload occurs when the amount of data an individual must take into account in order to make an adequate decision is larger than his capacity to process information (Heylighen, 2002). A study estimated that approximately 80% of college students are procrastinators, and procrastination was identified as one of the most common problem behaviors requiring improved management (Steel and Ferrari (2013) and Walker and Stewart (2000), as cited in Ko and Chang, 2018). Procrastinators display ineffective time and behavior management, which often results in counterproductive behaviors such as avoidance in starting or completing tasks, poor goals, or decisions (Howell and Watson (2007), Steel (2007), and Wolters (2003), as cited in Wang et al., 2015).

To date little is known as to how factors such as information overload and academic procrastination shape the information avoidance behavior of university students. Hence, this exploratory study purports to develop a model that examines the moderating effect of information overload and academic procrastination on the information avoidance behavior among Filipino undergraduate thesis writers. Results of this explanatory study can inform a number of sectors. First, librarians may be guided in planning projects and activities aimed at enhancing reading skills; promoting the importance of every information a user encounters; determining the preferences or demand of students and researchers when it comes to the materials they need, and hopefully expanding the study of information avoidance in the field of library and information science as well as in other fields. Second, teachers are invited to come up with a more integrated activity where reading strategies are embedded. Finally, parents are encouraged to be vital partners in nurturing in their children a reading culture that cuts across life activities leading to development of a positive outlook to reading.

Theoretical background

Theoretical framework

Edward Lee Thorndike’s Laws of Readiness and Effect underpin this inquiry. On one hand, the Law of Readiness states that when an organism is ready to act, it is reinforcing for it to do so and annoying for it not to do so. Also, when an organism is ready to act, forcing it to act will be annoying too (Olson and Hergenhahn, 2013). Further, the law states that learning is dependent upon the learner’s readiness to act, which facilitates the strengthening the bond between the stimulus and response (Oxford Dictionary of Sports Science and Medicine, 1993). In the context of this study, a motivated student who procrastinates may feel discomfort by doing something he is not ready to do. Also, a student who is not ready but forced to consume abundant information will most likely experience information overload that could lead to information avoidance. The Law of Effect, on the other hand, states that the strength of a connection is influenced by the consequences of the response (Olson and Hergenhahn, 2013) and that the strengthening and weakening of the connection largely depends on the accomplishment of a person at the end of the teaching process (Karadut, 2012). Students who avoid information brought about by information overload are likely those who failed to internalize and use effectively the information they consumed. Similarly, students who have effectively used different reading strategies and possess reading attitudes become more accomplished and often lead to academic success.

Reading strategy

Reading strategies are considered to be the indicators of how readers perceive a task, how they make sense of what they read, and what they do when they are unable to comprehend (Yukselir, 2014). It is referred to as behaviors that a reader engages in at the time of reading and that are related to some goals, or as the mental operations or processes involved when a reader purposefully approaches a text to make sense of what they read (Barnett, 1989; Cohen, 1990; Cook and Mayer, 1983).

Notably, effective readers were more aware of strategy use than less effective readers (Mokhtari and Reichard (2002), as cited in Nordin et al., 2013). This suggests that one needs to be a strategic reader to be an effective reader. Comprehensive studies on reading have shown that there are several strategies that could help a reader understand something. These include cooperative reading (Marzban and Akbarnejad, 2012), predicting, making connections, visualizing, inferring, questioning, summarizing (Block and Israel, 2005), contextualizing, de-contextualizing (Korkmaz, 2013), skimming, and scanning (Maxwell, 1972), among others. These, strategies need to be used to construct meaning effectively from any given written texts (Nordin et al., 2013).

While reading remains vital, understanding what is being read should not be overlooked. Simply failing to take the necessary steps to reveal the underlying meaning of what is being read can lead a reader to avoid reading critical information. Failure to identify effective reading strategies that cause poor reading comprehension would lead to information avoidance. Thus, a hypothesis was suggested:

H1. Better reading strategies lead to low information avoidance.

Reading attitude

Reading attitude is defined as “a system of feelings related to reading which causes the learner to approach or avoid a reading situation” (Alexander and Filler, 1976: 8) and “a state of mind, accompanied by feelings and emotions, that makes reading more or less probable” (Smith, 1990: 215). A number of studies revealed that parents and their practices in reading (kinalakihan) develop the attitude of students toward reading. Additionally, feelings and emotions also appear to be a factor affecting reading attitudes. Cross-sectional studies have shown that attitudes toward reading explain individual differences in reading behavior (Greaney and Hegarty (1987), Shapiro and Whitney (1997), and Stokmans (1999), as cited in Pfost et al., 2016) and the formation of attitudes themselves depends on cognitive, affective, and behavioral processes (Eagly and Chaiken (1993), as cited in Pfost et al., 2016). Notably, students’ own attitudes toward reading and the poor reading environments established by parents at home may contribute to poor reading habits (Morni and Sahari, 2013). Additionally, parents are an important source of information about reading and experiences with reading, thus helping form children’s attitudes toward reading (Baker, Scher and Mackler (1997), Hertel, Jude and Naumann (2010), and Stubbe, Buddeberg, Hornberg and McElvany (2007), as cited in Pfost et al., 2016). This may mean that students’ attitudes toward reading may emerge or change if they learn new information that forms or changes their beliefs that are associated with reading, due to the emotions they experience while reading or due to prior reading behavior (Pfost et al., 2016). This paper will explore the impact of reading strategies and information avoidance:

H2: Positive reading attitudes lead to better reading strategies.

H3: Positive reading attitudes lead to low information avoidance.

Academic procrastination

Procrastination is defined as the tendency to postpone or delay something currently unpleasant or difficult to do but is necessary to reach a certain goal, despite being aware of negative outcomes (Ainslie, 2008; Kağan et al., 2010; Lay, 1986; Steel, 2007). A number of researchers have identified procrastination in academic situations as a prevailing phenomenon also known as academic procrastination (Kármen, et al, 2015; Milgram et al., 1998). Numerous researches have defined academic procrastination as setting aside academic tasks, such as preparing for exams and doing homework up to the last minute and to feel discomfort out of this (Rothblum et al., 1986; Schouwenburg, 1995; Solomon and Rothblum, 1984). Further, it is described deliberately as delaying one’s tasks on academic issues in fear of making mistakes (Schouwenburg (1992) and Senecal et al. (1995), as cited in Çapan, 2010).

Recent studies have shown that most college students procrastinate in their academic activities (Özer, and Saçkes, 2011; Schouwenburg et al., 2004). Most of the existing literature on academic procrastination were linked to negative outcomes which contributed negatively to academic failure (Burka and Yuen, 1983; Ferrari et al., 1995; Knaus, 1998), poor academic performance, missing or late assignments, cramming, anxiety during tests, difficulties in following instructions (Kandemir, 2010; Özer, and Saçkes, 2011; Shih, 2017), falling behind in class (Rothblum et al., 1986), not attending school and dropping out of school (Knaus, 1998), restless nights, high levels of stress, regret, panic, and withdrawal due to lack of time (Jadidi et al., 2011). Milgram et al. (1995), for their part, found that it is concerned with bad time management. Solomon and Rothblum (1984) distinguished three areas that are likely to lead to information avoidance: writing papers, reading assignments, and studying for exams. However, Chu and Choi (2005) noted that procrastination is beneficial to some students in terms of working under time pressures, and some actively choose to procrastinate. Additionally, it has been found that people who have higher levels of hope tend to procrastinate less when they study for exams or write term papers. In fact, hope has been recognized as both an antecedent to coping and a coping strategy (Farran et al., 1995). Saddler and Buley (1999) noted that academic procrastination should also be related to other personal factors more specific to academic environment such as one’s reasons for engaging in learning, efforts and abilities. Based upon these discussions, a hypothesis was suggested:

H4: High tendency to procrastinate negatively moderates the relationship between reading strategies and information avoidance.

Information overload

Previous studies have indicated that despite numerous researches on information overload across different subjects, no single definition was accorded (Bawden and Robinson (2009), Edmunds and Morris (2000), and Shachaf et al. (2016), as cited in Jackson and Farzaneh, 2012;). However, Allen and Wilson (2003) defined it as a perception on the part of the individual where the flow of information associated with work tasks is greater than can be managed effectively, and a perception that overload in this sense creates a degree of stress for which his or her coping strategies are ineffective. Rogers (1986, as cited in Case, 2012), for his part, defined it as the state of an individual or system in which excessive communication inputs are processed, leading to breakdown. Tidline (2009) and Hoq (2016) added that information overload occurs when an individual is overwhelmed by the quantity of available information. Consequently, information will be ignored, forgotten, distorted or lost (Heylighen, 2002).

Chen et al. (2011) named four dimensions of potential contributors to students’ perceived information overload: limited learner readiness, quantity of information, quality of information, and medium interface. Heylighen (2002) asserted that the problem of information overload is that the amount of data and options that an individual must take into account in order to make an adequate decision is larger than that individual’s capacity for processing the information. Bawden and Robinson (2009) posited consequences of information overload such as infobesity, information avoidance, information anxiety, and library anxiety. In experiencing information explosion, information avoidance may be present, ignoring relevant valuable information and useful information sources because there is too much information to deal with (Bawden and Robinson, 2009; Hoq, 2016). Further, Mostak and Hoq (2014) enumerated five causes of information overload: multiple sources of information, too much information, difficult to manage information, irrelevance or unimportant of information, lack of time to understand information. Based on the foregoing discussions, a hypothesis was proposed:

H5: Excessive information ingestion negatively moderates the relationship between reading attitudes and information avoidance.

Information avoidance

Though several studies have been conducted to elucidate information avoidance, there is no exact description that can define the said concept accurately. As a concept, it entails preventing or delaying “the acquisition of available but potentially unwanted information”, either temporary or permanent, and it involves failing to acquire the information altogether (Shepperd et al. (2010), as cited in Howell et al., 2014). It is a broader construct representing the choice between seeking and avoiding information in a variety of contexts and not just health information and it represents the tradeoff between the resources required to cope with learning the information, and the resources people feel they have (Howell et al., 2014).

Information avoidance behavior consists of two aspects, namely: passive information avoidance and active information avoidance. Active information avoidance is a short-term behavior that is exhibited when a person avoids certain kinds of information that is thrust upon them occasionally under non-trivial circumstances and mostly includes information related to serious illness, or very personal matters like relationships or finances. Passive information avoidance, on the other hand, is a habituated long-term behavior that is exhibited when a person avoids certain kinds of information that he/she encounters everyday from being processed cognitively for so long that it becomes a passive and voluntary behavior in him and mostly includes information related to religious and political beliefs and world-wide view (Narayan et al., 2011).

Although one might view information avoidance as a straightforward matter of simply not looking, there are many other tactics that people can and do use to avoid information. Physical avoidance occurs when people are given a choice to avoid reading specific newspapers or magazines, listening to specific radio or television shows, or having conversations with certain people. Self-handicapping is a highly specialized form of information avoidance that is difficult to classify into broader categories. It refers to people’s tendency to choose tasks that are poorly matched to their own abilities – either too easy or too difficult – or to take actions that undermine their performance, as a strategy for avoiding information about their abilities (Golman et al., 2017).

Despite the considerable amount of discussion about information overload and its associated quality issues, a paucity of research exists on the fact that sometimes people avoid information, if paying attention to it will cause mental discomfort, cognitive dissonance, or increase uncertainty, irrespective of the utility of the information (Narayan et al., 2011). Individuals generally tend to expose themselves to information that is already in accordance with their interests, needs, or existing attitudes and avoid information that contradicts them, thus employing selective exposure, and consciously or unconsciously avoiding or rejecting information that does not agree with their world-view (Rogers, 1983 as cited in Narayan et al., 2011). Psychologists have documented that information-seeking is influenced by stress and includes coping mechanisms such as repressing, blunting, and rejecting information wherein a person voluntarily or involuntarily blocks out some informational fields and pathways, including information that may help prevent terrorist attacks and prepare for disasters (Covington and Mueller (2001), Khrone (1993), and Miller and Mangan (1983), as cited in Narayan et al., 2011). Based on the foregoing discussions, a hypothesis was proposed:

H5: Excessive information ingestion negatively moderates the relationship between reading attitudes and information avoidance.

The foregoing research arguments are captured in the hypothesized model shown in Figure 1.

Figure 1.

The hypothesized model.

Methods

Research design, study site and subjects

The overall intent of the study was to examine how information overload and academic procrastination affect the development of information avoidance behavior among a select group of Filipino undergraduate thesis writers when reading journal articles. To achieve this purpose, Structural Equation Modelling (SEM) was employed. As an approach, it combines complex path models with latent variables (factors). Using SEM, researchers can specify confirmatory factor analysis models, regression models, and complex path models (Hox and Bechger, 1999). The purpose of SEM is to define a theoretical causal model consisting of a set of predicted covariances between variables and then test whether it is plausible when compared to the observed data (Jöreskog, 1970; Wright, 1934).

The locus of the study is a comprehensive university situated in the capital of Manila. It is one of the largest universities consisting of almost 44,000 students, the oldest university in Asia, and is ranked among the top 1000 universities in the world. A total of 215 respondents were purposively recruited from one of the colleges with diverse program offerings, namely: Bachelor of Secondary Education, Bachelor of Elementary Education: Major in Pre-School Education, Bachelor of Elementary Education: Major in Special Education, Bachelor of Science in Food Technology, Bachelor of Science in Nutrition and Dietetics, and Bachelor of Library and Information Science. To qualify, respondents must be: (a) undergraduate students of the respondent university, (b) Filipino, and (c) currently enrolled in thesis writing. Initially, a total of 300 questionnaires were distributed; however, only 215 questionnaires were retrieved and were considered usable.

Sample size was calculated based on the need to conduct the SEM analysis which means that a minimum of 200 subjects was necessary for adequate model specification (Buhi et al., 2007).

Data measures

Robotfoto

The first part of the interview made use of a robotfoto, a Dutch term which refers to a cartographic sketch of a criminal suspect based on the description of the witnesses (Kelchtermans and Ballet, 2002). It consists of the respondents’ demographic data, which includes their gender, age, field of study, year they started to use databases, allotted time, format of journal article, section of the article that the students read first and read most, part of the journal article that the students find difficult to read and the section of the article that the students find relevant to the research.

Information Avoidance Scale

To measure the information avoidance behavior of the respondents, a researcher-made tool was developed. It consists of 53 items that can be accomplished through an 8-point Likert scale, with 1 indicating agreement to a much extent and 8 signifying disagreement. Such practice is based on Matell and Jacoby’s (1972) assertion that as the number of scale steps is increased, respondents’ use of midpoint category decreases. Results of Cronbach Alpha test showed an acceptable reliability coefficient of 0.785.

Information Overload Scale

The Information Overload Scale developed by Williamson and Eaker (2012) was used to measure information overload. Representative statements of Information Overload Scale were: “There is so much information available on topics of interest to me that I have trouble choosing what is important and what’s not”, and “I have so much information to manage on a daily basis that it is hard for me to prioritize tasks”. It is an adapted14-item tool with a Cronbach Alpha value of .90.

Academic Procrastination Scale

To measure the academic procrastination variable, the Academic Procrastination Scale developed by Justin McCloskey (2011) was adapted. Originally, it consisted of 25 items and made use of a 5-point scale. In this study, an 8-point Likert scale was observed. The items include “l put off projects until the last minute”, “I know I should work on schoolwork, but I just don’t do it”, “I get distracted by other, more fun things when I am supposed to do work on schoolwork”, “When given an assignment, I usually put it away and forget about it until it is almost due”, and “I frequently find myself putting important deadlines off”. As reported by previous studies, the Cronbach alpha value of this scale is 0.94.

Survey of Adolescent Reading Attitude (SARA)

This instrument was adapted by the researchers to examine the respondents’ reading attitude towards reading journal articles. Developed by Conradi et al. (2013), it consists of 25 items accomplished through a 6-point Likert scale with a Cronbach Alpha value of 0.96.

Metacognitive Awareness of Reading Strategies Inventory (MARS) Version 1.0

To evaluate the respondents’ practices and behavior towards reading journal articles, the researchers adopted the MARS Version 1.0 tool. It is a 30-item tool made by Kouider Mokhtari and Carla Reichard (2002) and can be accomplished through a 5-point scale. As reported by previous studies, the Cronbach Alpha value is 0.89.

Data collection and ethical considerations

Data needed for the study was gathered from a pool of 215 senior students enrolled in thesis writing. A room-to-room distribution was conducted under the supervision of the course facilitators and class presidents. Any questions that the respondents had problems with were immediately addressed by the researchers. The survey lasted for about 30 minutes. Preliminarily, a letter of request was sent to the office of the dean requesting endorsement for a 2-week data gathering.

Data analysis

Fifty-three information avoidance items, 30 reading strategies items and 14 information overload items were factor analyzed using the principal axis factoring and varimax rotation procedure in order to delineate underlying dimensions of information avoidance, reading strategies and information overload associated with thesis writers. The most common and reliable criterion is the use of eigenvalues in extracting factors. In this research, all factors with eigenvalues greater than 1 were retained, because they were considered significant; all factors with less than 1 were discarded. Additionally, all items with a factor loading above 0.4 were included, whereas all items with factor loadings lower than 0.4 were removed. A reliability coefficient (Cronbach’s alpha) was computed for each factor to estimate the reliability of each scale. All factors with a reliability coefficient above 0.6 were considered to be acceptable in this study. In this study, 10 items were discarded since their reliability coefficient was below 0.6.

Descriptive statistics for the major study variables, demographics, and Cronbach alpha reliability estimates were conducted using the Statistical Package for the Social Sciences (SPSS) (version 24). The hypothesized model in the study was examined using the SEM procedures using the WarpPLS software program (version 5.0).

Results

Table 1 depicts the demographic profile of the respondents. The majority are female (n=183, 85.1%) while more than half (113 or 52.6%) are from the food technology and nutrition programs and belong to the age group 19 to 20 years old (186 or 86.5%). Most of the respondents started using databases for academic purpose during their freshmen year (n=140 or 60.5%) in college. Of the respondents 58 (20%) indicated that they spend 1 to 2 hours (120 minutes) searching for journal articles on a daily basis, while 11 (5.1%) do not use e-databases. Most respondents mainly use Science Direct (n=165, 76.7%) when looking for journal articles, followed by EBSCO (n=121, 56.3%). More students also prefer the printed version (n=126, 58.6%) of the articles. Respondents read the Abstract section first (n=175, 64.6%) and the Discussion section (n=68, 23.9%) the most; 79 (32.4%) of the respondents find the Findings/Results section difficult to read. However, only 73 (23.9%) of them responded that the Findings/Results section is relevant to their research undertaking.

Table 1.

Demographic profile of respondents (n=215).

Profile	N	%	Profile	n	%
Gender			Section of the article that the students read first
Male	29	13.5	Abstract	175	64.6
Female	183	85.1	Introduction	31	11.4
Age			Review of literature	7	2.6
18	3	1.4	Method	9	3.3
19	79	36.7	Findings/Result	13	4.8
20	107	49.8	Discussion	14	5.2
21	21	9.7	Conclusion	18	6.6
22	1	.5	Reference	4	1.5
23	1	.5	Section of the article that the students read most
Field of study			Abstract	42	14.8
Food Technology	67	31.2	Introduction	26	9.2
Nutrition and Dietetics	46	21.4	Review of literature	13	4.6
Secondary Education	3	1.4	Method	17	6
Primary Education	58	27	Findings/Result	57	20.1
Special Education	41	19.1	Discussion	68	23.9
Year started to use database			Conclusion	58	20.4
1st Year	130	60.5	Reference	3	1.1
2nd Year	29	13.5	The part of the journal article that the students find difficult to read
3rd Year	19	8.8	Abstract	8	3.3
4th Year	36	16.7	Introduction	8	3.3
Allotted time in minutes on using e-databases			Review of literature	49	20.1
30 minutes or less	26	12.1	Method	65	26.6
31 to 1 hour	45	21	Findings/Result	79	32.4
1 hour to 2 hours	58	27	Discussion	30	12.3
2 hours to 3 hours	42	19.6	Conclusion	3	1.2
More than 3 hours	40	18.6	Reference	2	.8
E-Databases used*			Section of the article that the students find relevant to the research
EBSCO	121	56.3	Abstract	35	11.4
JSTOR	37	17.2	Introduction	15	4.9
Science Direct	165	76.7	Review of literature	35	11.4
GALE	31	4.4	Method	26	8.5
Others	19	8.83	Findings/Result	73	23.9
Format of the journal article			Discussion	71	23.2
Printed version	126	58.6	Conclusion	41	13.4
Electronic version	79	36.7	Reference	10	3.3

Multiple responses.

Table 2 presents the Regression Weights of Reading Attitudes indicators of Filipino undergraduate thesis writers. As shown the fourth indicator, reading journal articles outside of school, got the highest regression weight of 0.143 while the third indicator, reading journal articles to do research for a class, got the lowest regression weight (0.088).

Table 2.

Regression weights of indicators of reading attitudes of Filipino undergraduate thesis writers.

Reading attitudes	Standardized regression weights	Cronbach alpha
		0.881
Prevailing feelings or emotions		0.688
talking with friends about something you’ve been reading in your free time	0.142
reading journal articles in your free time	0.136
Reading environment		0.716
reading journal articles outside of school	0.143
reading journal articles with friends	0.141
Reading habits		0.755
reading journal articles for a course	0.11
reading journal articles online for class	0.11
reading journal articles for fun	0.13
reading journal articles to do research for a class	0.088

Table 3 shows the standardized regression weights of the variable Academic Procrastination. It shows that the fourth indicator, “when given an assignment, I usually put it away and forget about it until it is almost due”, got the highest regression weight of 0.221. The third indicator, “I get distracted by other, more fun things when I am supposed to do work on schoolwork” got the lowest regression weight (0.172).

Table 3.

Regression weights of indicators of academic procrastination of Filipino undergraduate thesis writers.

Academic procrastination	Standardized regression weights	Cronbach alpha
l put off projects until the last minute	0.184	0.905
I know I should work on schoolwork, but I just don’t do it	0.217
I get distracted by other, more fun things when I am supposed to do work on schoolwork	0.172
When given an assignment, I usually put it away and forget about it until it is almost due	0.221
I frequently find myself putting important deadlines off	0.206

Table 4 shows the results of the factor analysis of why Filipino undergraduate thesis writers avoid information. Factor 1 was labeled as “Details-Driven Information Avoidance” which explained 42.9% of the total variance with a reliability coefficient of 0.78. This factor pertains to the way students scrutinize the journal articles by taking notes, examining and paying attention to details. “Magnitude-Driven Information Avoidance” which accounted for 6.8% of total variance with a reliability coefficient of 0.78 is under factor 2. It refers to the tendency of students to accumulate a massive amount of information regardless of its format, accessibility, topic and other specific details. Factor 3 labeled as “Alignment-Driven Information Avoidance” typifies students’ tendency to observe authors use of writing devices, topic-design alignment, argumentation, synoptic interpretation, instrumentation and cross-referencing practice. Factor 4 or “Consciousness-Driven Information Avoidance” refers to students’ awareness and use of available articles written by prolific authors with new and novel designs. Factor 5 is labeled as “Architecture-Driven Information Avoidance” refers to students’ tendency to read articles based on the format, length, and convincing power. The “Condition-Driven Information Avoidance” with a total variance of 2.635% constitutes factor 6. It refers to students’ awareness to acquire articles if certain conditions are to be met. However, 10 variables were removed, namely: “I read articles that are funded only”, “I read articles with abstract that follows a structured format (background, objectives, methods, results, conclusion. . ..)”, “I read articles done empirically”, “I pay attention to the trends and issues surrounding my topic”, “I read articles with a few number of pages”, “I read articles that come in two columns”, “I read articles that come in one column”, “I read articles that negate my existing conception about the topic”, “I read articles done conceptually including commentaries”, and “I read articles with abstract that reports statistical data”.

Table 4.

Exploratory factor analysis of information avoidance of Filipino undergraduate thesis writers.

Information avoidance	Factor loading	Eigen value	% Variance	Cronbach alpha
				0.785
In writing my thesis project. . .
FACTOR 1: DETAILS-DRIVEN INFORMATION AVOIDANCE		4.27	42.968	0.698
I take note of the findings of the study in relation to the research questions	.739
I pay attention to the major conclusions derived from the paper	.736
I read the implications of the study to theory and practice	.721
I take note of the relationship/differences of the articles I read from my previous investigations	.693
I pay attention to the recommendations and suggestions of the author	.654
I examine how authors presented their data either through figures or tables	.652
I examine the recency and strength of the references used by the author/s	.650
I pay attention to the way figures are presented, analyzed and interpreted	.643
I pay attention to the conflicting results reported previously	−.610
I take note of statistically significant and non-significant results of the study	.602
I try to figure out how the procedures in the article may apply to my research project	−.574
I consider the keywords used by the authors	−.535
I examine the nature of the research questions raised by the authors	.519
I take note of the contributions of the paper to theory, research, practice or policy	−.513
I pay attention to the statistical tools used in the paper in relation to the study objectives	.495
FACTOR 2: MAGNITUDE-DRIVEN INFORMATION AVOIDANCE		2.116	6.856	0.709
I read articles regardless of the country where they come from	.727
I read articles regardless of the authors who write it	.673
I read articles despite the technical jargons used	.641
I read articles with multiple authorship	.635
I read articles provided by open access databases	.559
I read articles that deal with controversial topics related to my research topic	.550
I try to search as many journals as I can regarding my chosen topic of area	.539
I only read articles that directly relate to my topic	−.520
I read articles published by journals with high impact factor	.504
I only open journals related to my research topic	−.476
FACTOR 3: ALIGNMENT-DRIVEN INFORMATION AVOIDANCE		3.845	5.765	0.791
I pay attention to the inclusion and exclusion criteria used in the article	.751
I look at the alignment of the design used in the paper to state objectives or purpose	−.688
I pay attention to the hedging devices (ex. seems, apparently, would, could, may, etc.. . .) used by the author	.680
I examine how each of the variables in the study were measured by the tools	.676
I pay attention to how authors summarize previous studies pertaining to a particular variable	.651
I pay attention to the procedures followed by the authors	.636
I pay attention to how the study arguments in the paper came about	.626
I love to read articles with a good number of cross-references	.513
FACTOR 4: CONCIOUSNESS-DRIVEN INFORMATION AVOIDANCE		4.449	4.237	0.883
I have a listing of journals in my discipline	.722
I read articles written by prolific writers in my discipline	.700
I look for all the journals in my discipline	.694
I am aware of the different journals in my discipline	.642
I read articles given for free by journals in my discipline	.635
I read articles that use new and emerging research designs	.578
I explore the articles cited by the authors to support my current study	.478
FACTOR 5: CONDITION-DRIVEN INFORMATION AVOIDANCE		2.015	2.635	0.858
If the journal article charges a fee, I still purchase it	.897
If the journal article charges a fee, I write to the reference librarian for a copy	.895
I email the author for a copy of the article when not available online	.815

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = .926.

Table 5 shows the exploratory factor analysis of the reading strategies of Filipino undergraduate thesis writers. Factor 1, which yielded eight variables, describes the strategies to remain focused. Focus-driven reading strategy refers to the tendency of thesis writers to adapt ways to remain focused on their work which include recalling, adjusting, and using contextual clues. Gist-driven reading strategy indicative of six variables under factor 2 provided ways on how to understand information easier. It could either be through analysis or through visual/typographical aids. Factor 3 or purpose-driven reading strategy yielded seven variables, refers to how the respondents achieve their purpose when they read through previewing and reading thoroughly to understand what the text is all about. The five variables under factor 4 were described as interrogating-driven reading strategy which typifies respondents’ use of prediction, discussion, and note-taking strategies to check their understanding of the text. Reflective-driven reading strategy indicative of three variables under factor 5 describes respondents’ use of skimming or reflection when reading text.

Table 5.

Exploratory factor analysis of reading strategies of Filipino undergraduate thesis writers.

Reading strategies	Factor loading	Eigen value	% Variance	Cronbach alpha
				0.912
FACTOR 1: FOCUS-DRIVEN READING STRATEGY		5.961	47.927	0.918
I adjust my reading speed according to what I’m reading	.809
I try to get back on track when I lose concentration	.759
I use reference materials such as dictionaries to help me understand what I read	.714
I underline or circle information in the text to help me remember it	.696
I think about whether the content of the text fits my reading purpose	.675
When text becomes difficult, I pay closer attention to what I’m reading	.674
I go back and forth in the text to find relationships among ideas in it	.593
I use context clues to help me better understand what I’m reading	.589
I decide what to read closely and what to ignore	.452
FACTOR 2: GIST-DRIVEN READING STRATEGY		3.766	6.460	0.891
I paraphrase (restate ideas in my own words) to better understand what I read	.692
I critically analyze and evaluate the information presented in the text	.682
I check my understanding when I come across conflicting information	.641
I try to picture or visualize information to help remember what I read	.609
I use typographical aids like bold face and italics to identify key information	.577
I use tables, figures, and pictures in text to increase my understanding	.565
FACTOR 3: PURPOSE-DRIVEN READING STRATEGY		4.291	4.798	0.888
I have a purpose in mind when I read	.700
I read slowly but carefully to be sure I understand what I’m reading	.690
I preview the text to see what it’s about before reading it	.643
I think about what I know to help me understand what I read	.616
I try to guess what the material is about when I read	.571
When text becomes difficult, I re-read to increase my understanding	.569
When text becomes difficult, I read aloud to help me understand what I read	.502
FACTOR 4: INTERROGATING-DRIVEN READING STRATEGY		3.521	4.071	0.832
I ask myself questions I like to have answered in the text	.736
I check to see if my guesses about the text are right or wrong	.734
I try to guess the meaning of unknown words or phrases	.713
I take notes while reading to help me understand what I read	.644
I discuss what I read with others to check my understanding	.694
FACTOR 5: REFLECTION-DRIVEN READING STRATEGY		1.612	3.613	0.742
I summarize what I read to reflect on important information in the text	.612
I skim the text first by noting characteristics like length and organization	.514
I stop from time to time and think about what I’m reading.	.486

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = .944.

Table 6 presents the results of the factor analysis of information overload among Filipino undergraduate thesis writers. Factor 1 yielded nine variables, with a 57.275% variance. Labeled as information osmosis, it describes the users’ difficulty in dealing with the huge amount of information through difficulty in concentration, assimilation, and prioritization. With a 7.604% variance, factor 2 called as information paralysis typifies a state where users are able to take action, pay attention on, and be abreast of the new trends due to vastness and hugeness of information made available to them. The five variables show how over-analyzing of information lead to numbness, incapacity for action and being pressed for time.

Table 6.

Exploratory factor analysis of information overload of Filipino undergraduate thesis writers.

Information overload dimensions	Factor loading	Eigen value	% Variance	Cronbach alpha
				0.869
INFORMATION OSMOSIS		6.272	57.275	0.920
There is so much information available on topics of interest to me that I have trouble choosing what is important and what’s not	.846
It is sometimes hard for me to concentrate because of all the information I have to assimilate	.799
I regularly feel overwhelmed by too much information these days	.777
I have to process so much information that it frequently takes me too long to get things done in a timely manner	.764
I have to manage so much information in my daily life that it takes me a long time to complete even simple tasks	.688
I am stressed out by the sheer volume of information I have to manage on a daily basis	.671
I feel overwhelmed learning a new subject or topic because there is so much information	.638
I have so much information to manage on a daily basis that it is hard for me to prioritize tasks	.594
When I search for information on a topic of interest to me, I usually get too much rather than too little information	.495
INFORMATION PARALYSIS		3.705	7.604	0.883
I sometimes feel numb and incapable of action because of all the information I have to process on a daily basis	.807
I feel like my attention span is becoming shorter and shorter because of information overload	.804
I feel like I can’t keep up with all the new developments in my area of expertise	.776
I regularly feel pressed for time because of all the information I have to deal with	.738
It seems like the volume of information available is increasing exponentially in a relatively short period of time	.580

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = .935

The fit indices of the emerging model (see Table 7) reflect the model fit statistics of the resulting model, namely: average path coefficient (APC), average R-squared (ARS) and average block (VIF). The APC value (0.294) and ARS value (0.385) are having the same p value of 0.0011 which indicates the fitness of the proposed model. AVIF value (1.316) is acceptable since it is less than 3. Schermelleh-Engel et al. (2003) stated that this ratio indicates good fit when it produces 2 or a smaller value while it indicates an acceptable value when it produces a value of 3. Ding et al. (1995) averred that this ratio should be close to 1 or at least have a smaller value. Other fit indices, specifically AARS (0.379), AFVIF (1.646), RSCR (0.971), SSR (1.000) and NLBCDR (1.000), all revealed acceptable results that showed support of the proposed model. However, the SPR value of the model is 0.600 which is below the acceptable rate of 0.7. Since majority of the fit indices show acceptable results, then the model is deemed to be fit.

Table 7.

Fit indices of the emerging model.

Model fit indices	Model
Average path coefficient (APC)	0.294
Average R-squared (ARS)	0.385
Average adjusted R-squared (AARS)	0.379
Average block VIF (AVIF)	1.316
Average full collinearity VIF (AFVIF)	1.646
Sympson’s paradox ratio (SPR)	0.600
R-squared contribution ratio (RSCR)	0.971
Statistical suppression ratio (SSR)	1.000
Nonlinear bivariate causality direction ratio (NLBCDR)	1.000

Relationships between the factors of information avoidance are shown through an emerging model in Figure 2. Of the five proposed hypotheses, only four were supported: (H2) Positive reading attitudes lead to better reading strategies, (H3) Positive reading attitudes lead to low information avoidance, (H4) High tendency to procrastinate negatively moderates the relationship between reading strategies and information avoidance, and (H5) Excessive information ingestion negatively moderates the relationship between reading attitudes and information avoidance.

Figure 2.

The emerging model.

On the one hand, reading attitudes (β = 0.48) have a direct moderate effect on reading strategies; therefore, when students have a positive attitude towards reading, the more likely they employ better reading strategies. Reading attitudes (β = 0.48) have a direct moderate effect on information avoidance; therefore, when students exhibit positive attitude towards reading, the less likely they are to exhibit information avoidance. Academic procrastination (β = 0.03) has a direct but negligible effect on the moderating relationship between reading strategies and information avoidance; therefore, the tendency to procrastinate has less effect the relationship between reading strategies and information avoidance. Excessive information (β = 0.09) has a direct negligible effect on the moderating relationship between reading attitudes and information avoidance. Hence, the tendency to procrastinate and acquire excessive information has less effect on the relationship between reading attitudes and information avoidance. On the other hand, reading strategies (β = -0.47) have a weak inverse effect on information avoidance; therefore, the more reading strategies are used, the lower is information avoidance.

Discussion

First, this study shows that reading strategies were found to have a direct weak effect on information avoidance. As pointed out by various researchers, for successful reading to take place, it is not only necessary to know which strategies to use but also how to use them appropriately (Aarnoutse and Schellings, 2003; Anderson, 1991; Carrell, 1998; Garner, 1994; Kong, 2006; Liu et al., 2014; Paris et al., 1994; Talebinejad et al., 2015). To achieve a better understanding of the text and reduce reading time, readers tend to use strategies that enable them to determine useful texts that satisfy their information needs (Afflerbach et al., 2008; Albiladi, 2018; Koch and Spörer, 2017; Nordin et al., 2013; Shehata, 2017; Talebinejad et al., 2015; Weinstein and Hume, 1998). As disclosed by the profile of the respondents in the study (see Table 1), the Filipino undergraduate students posted a higher percentage on the use of different reading strategies like skimming and scanning which implies a lesser tendency to avoid information. Said skills are vital, relevant, and useful as they navigate the information presented in the article being read. Consequently, they are able to acquire better understanding of the article and make sense of the information as they write their thesis. Impliedly, university faculty are encouraged to make journal readings integral in the learning experiences of the students so as to maximize students’ use of a number of reading strategies.

Second, this study shows that reading attitudes have a direct moderate effect on reading strategies. Therefore, when students have a positive attitude towards reading, the more likely they employ better reading strategies. According to various scholars, learner attitudes and motivation are closely associated with student learning and use of strategies (Alexander et al., 1998; Carr and Borkowski, 1989; Gan et al., 2004; Gao, 2003; Guthrie and Wigfield, 2000; Pressley, 1998; Proctor et al., 2006; Stephens et al., 2015; Van Elsäcker, 2002; Van Kraayenoord and Schneider, 1999; Weinstein and Hume, 1998; ). A number of studies have also reported that researchers nowadays practice different ways when it comes to reading (Urquhart and Weir, 2014; Wohl and Fine, 2017; Zachariah, 2016), and these are: highlighting (Vafeas, 2013), which include scanning (Liu, 2005), skimming, search reading, and keyword spotting (Liu, 2005), reading textbooks (Vafeas, 2013), careful reading, and browsing (Choo et al., 1999; Ellis, 1989; Foster, 2005; Liu, 2005; Schmar-Dobler, 2003; Shehata, 2017; Urquhart and Weir, 2014; Wohl and Fine, 2017), screen-based reading (Liu, 2005), and skim-reading (Fitzsimmons et al., 2014) of information on the Internet (Baer et al., 2007; Barab et al., 1999; Calisir and Gurel, 2003; Coiro and Dobler, 2007; Hofman and Oostendorp, 1999; McDonald and Stevenson, 1998; Mohageg, 1992; Ozok and Salvendy, 2003; Spruijt and Jensen, 1999; Van Nimwegen et al., 1999; Waniek et al., 2003; Zhang and Duke, 2008; Zumbach et al., 2001), where students agree that making the text available online offers many benefits for the readers (Grzeschik et al., 2011, Shehata, 2017). In this study, 27% of the undergraduate thesis writers allotted one to two hours in reading journal articles which implies the need to address their seemingly positive attitude towards reading. At the university level, the practice of assigned journal readings and access to a wide range of electronic databases can expectedly nurture in the students the love for reading and information seeking that is evidence based. Additionally, parents are hereby encouraged to motivate and engage their children to read at an early age to enkindle their desire to read, manifest love for reading, and hopefully pick up various reading strategies that will shape their reading attitude, style, or practice along the way.

Third, this study shows that reading attitudes have a direct moderate effect on information avoidance. Therefore, when students exhibit a positive attitude towards reading, the less likely they are to exhibit information avoidance. Learners who habitually avoid reading practice rarely become skilled readers (Broekhof, 2011; Guthrie et al., 2001; Huysmans, 2013; Juel, 1988; McKenna et al., 1995 Meelissen, et al., 2012; Melnick et al., 2009; Mol and Jolles, 2014; Stanovich, 1986; Stokmans, 2006) and those with weaker reading skills feel less positive towards reading and are therefore less likely to read and practice their reading skills (Broekhof, 2011; Huysmans, 2013; McKenna et al., 1995; Meelissen, et al., 2012; Melnick et al., 2009; Mol and Jolles, 2014; Stokmans, 2006). Impliedly, teachers at whichever level of education are expected to create a learning atmosphere where the love for reading becomes a habit, and the practice of reading is viewed as a facilitative tool for learning and a survival kit for meeting the needs of an information-driven world. Librarians, for their part, should deepen in the students various reading skills through a clear-cut and programmatic library activities that are user-friendly and reading-centered.

Fourth, this study shows that academic procrastination has a direct but negligible moderating effect on the relationship between reading strategies and information avoidance. Therefore, the tendency to procrastinate has less effect on the relationship between reading strategies and information avoidance. Negative emotions and personality traits (Clark and Hill, 1994) such as anxiety, disturbance, depression, and disappointment (Aziz and Tariq, 2013; Cassady and Johnson, 2002; Chabaud et al., 2010; Owens and Newbegin, 1997; Solomon and Rothblum, 1984) vis-a-vis stress, frustration, feelings of annoyance, indecision decisions, and task avoidance (Grunschel et al., 2013; Kliengsiek et al., 2013) have been identified as factors of procrastination. In this study, 20% of the respondents indicated that they spend one to two hours (120 minutes) searching for journal articles on a daily basis on top of the other priorities and activities they have within the day. Previous studies have indicated that surfing the Internet and communicating online are some tasks that lead to procrastination (Grund et al., 2012, Lavoie and Pychyl, 2001; Meier et al., 2016). When procrastinating, Turkish adolescent boys were more likely to spend time with electronic media (watching TV, emailing, going on-line, and, in particular, playing computer games), whereas girls were most likely to read books, magazines, and newspapers (Klassen and Kuzucu, 2008). In the Global Digital 2019 report, Filipinos were found to spend an average of 10:02 hours a day on the Internet – on any device (CNN Philippines, 2019) making the Philippines the heaviest Internet user in the world. Notably, Filipinos spend more than 3.5 hours a day on social media, which is the highest among the 30 economies listed. Hence, engaging in social media-related activities has become a regular habit and students will continue to use it even if it is not during leisure time (Dumpit and Fernandez, 2017). As more faculty members are using ICT tools in their teaching, students expect that the university would provide them access to computers with superior Internet connection. In contrast, students from private higher education institutions (HEIs) may have access to the Internet through home and/or mobile data subscription, and are, therefore, not solely dependent on the school’s network for Internet access (Dumpit and Hernandez, 2017). Academic procrastination among Chinese international students has been considered as one possible consequence of stress and difficulty resulting from the acculturation and cultural adjustment process (Lowinger et al. (2014), as cited in Lowinger et al., 2016). In today’s era of massive use of the Internet of things, librarians are expected to ensure the maximum effective use of electronic databases as vital and accurate sources of information as well as function, promote activities on the internet that could assist students in meeting the information demands of their academic study. Instructors, for their part, should reflect on the design of academic tasks and at the same time provide students with a doable timetable to avoid delay or avoidance. Students should be given re-orientation on proper time management and on appropriate use of the internet to better promote productivity and mindfulness.

Lastly, this study shows that excessive information ingestion negatively moderates the relationship of reading attitude and information avoidance. Therefore, the ingestion of information by the students has less effect on the relationship between the reading attitude and information avoidance. Various scholars mentioned that people experiencing information overload may be the effect of data quality or quality of information (Chen et al., 2011; Hargittai et al., 2012; Lines and Denstadli, 2004), quantity of information (Burge (1994), as cited in Shrivastav and Hiltz, 2013), and cultural background (Haase et al., 2014), and time (Mostak and Hoq, 2014), among others. By having a positive attitude towards reading, students are less likely to experience information overload. They may also selectively read specific sections of the reading material depending on their motivation that supports their attitude and avoid information (Sawicki et al., 2013; Smith et al., 2007). Impliedly, librarians may plan activities for library users in developing their information-seeking skills in browsing print and electronic resources to prevent information avoidance and ensure better management of the information flow. Students must learn how to manage their time well and prioritize which activities should be focused on so that information that they are supposed to take in would not be too overwhelming and put into practice skills like reducing the intake of information, setting time limits for tasks, breaks, and focusing on the specific and essential sections of journal articles. Cutting down the number of websites to visit may also be helpful to minimize information overload.

Conclusion

The overall intent of the study is to examine how information overload and academic procrastination affect the development of information avoidance behavior among a select group of Filipino undergraduate thesis writers as well as their attitude and strategies toward reading journal articles. In this study, it was found that better reading strategies would lead to low information avoidance. Moreover, a positive attitude towards reading leads to better reading strategies. If the respondents have a positive attitude towards reading, they are less likely to exhibit information avoidance. Additionally, the tendency to procrastinate does not affect the relationship between reading strategies and information avoidance. Lastly, the ingestion of information of the students has less effect on the relationship between the reading attitude and information avoidance.

Against the backdrop of the results yielded in this study, the issue of information avoidance and its allied behaviors should be given utmost attention if today’s university students are to operate in an information-driven society. The ever-increasing expectations from today’s HEIs demand an academic platform that prepares students to be knowledge workers and information generators. Thesis writing as a gear of scholarship may be viewed as a means for building and nurturing healthy and proactive information-seeking behaviors among university students. While students are faced with a multitude of information sources, universities are expected to arm them with scaffolds and support measures that develop in them the skills for better information access, retrieval management, and sense-making, thus minimizing their tendencies to avoid information as a result of information overload. Exposure to the effective use of graphic organizers, graphic note-taking, and emerging reading strategies may be provided to ensure their success in doing university course work. University requirements are expected to create a balance in the use of relevant databases, and observe a more integrative approach to learning.

The emerging model yielded in this study invites further testing and validation in a variety of contexts vis-à-vis the use of a plurality of research designs for a more vivid portrait and dynamics of information avoidance in university settings. Replication of this study from a cross-cultural perspective may hold a number of promises in identifying key measures, policies, and programs that prepare students to operate efficiently and effectively in the various scholarships expected from today’s universities.

Footnotes

Acknowledgements

The authors would like to acknowledge Mr Symon Lagao for his contribution of ideas at the initial phase of this paper.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Allan B. de Guzman

Author biographies

Maria Cristina M. Fuertes obtained her Bachelor of Library and Information Science degree from the University of Santo Tomas. Her research interests include information avoidance, information seeking behavior, and information literacy.

Beatriz Marie D. Jose graduated from the University of Santo Tomas with a Bachelor of Library and Information Science degree. Her research interests include information behavior, library patronage, and information literacy.

Mary Angelie A. Nem Singh graduated from the University of Santo Tomas with a Bachelor of Library and Information Science degree. Her research interests include information literacy, library services and use, and information literacy.

Pauline Eirisse P. Rubio obtained her Bachelor of Library and Information Science degree from the University of Santo Tomas. Her research interests include information literacy, user satisfaction, and information literacy.

Allan B. de Guzman, PhD, handles pedagogy, management, and research courses at both graduate and undergraduate levels in the University of Santo Tomas. As a prolific writer in educational policy studies, adult learning, and teacher education, he has extensively published a total of 162 articles in various ISI-listed journals and at the same time serves as editor, board member, and reviewer in international journals. He has received various prestigious awards which included the 2007 SEAMEO-JASPER Research Award given by the Government of Canada and the Southeast Asian Ministers of Education Organization (SEAMEO), the 2006 National Research Council of the Philippines (NRCP)-Department of Science and Technology (DOST) Achievement Award and the 2011 Metrobank Foundation Search Most Outstanding Teacher Award in Higher Education, among others. He was the recipient of the 2104 Australian Awards Fellowship on Curriculum Leadership at Queensland University of Technology, Brisbane, Australia.

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