Mediation of information overload in the relationship between information-seeking and digital well-being: An exploratory study

Abstract

This article examined the digital well-being of citizens during a time of conflict and explored the relationships between citizens’ information-seeking behaviour, information overload and digital well-being. The study adopted a quantitative approach, collecting data through an adapted questionnaire distributed on social media. The researchers utilised the snowball sampling technique. A total of 155 respondents completed the copies of the questionnaire. The results show a positive correlation between information-seeking behaviour and information overload, as well as an unexpected positive correlation between information overload and digital well-being. This finding contrasts with previous studies that suggest a negative correlation between information overload and subjective well-being in both regular and crisis times. Furthermore, the results of the path analysis revealed that information overload serves as a mediator between information needs, information barriers and digital well-being. Individuals are advised to find a personal balance in managing information overload to enhance their digital well-being.

Keywords

Crisis digital well-being information overload information-seeking behaviour

1. Introduction

During crises, information-seeking plays a crucial role in keeping individuals informed, particularly when people actively seek information to ensure their safety [1]. On 7 October 2023, the Hamas terrorist organisation invaded Israel, brutally massacring and attacking civilians in southern Israel. In total, 1300 Israelis were murdered, and hundreds were abducted into Gaza [2]. This attack caused many Israelis to turn to their television screens, while others sought to comprehend the events in real time by using various platforms such as TikTok and Instagram [3].

The current study delves into digital well-being in a conflict and aims to examine the associations between citizens’ information-seeking behaviour, information overload and digital well-being.

Information-seeking behaviour is the purposeful process of searching for information to achieve a specific goal, which may require engagement with both traditional and digital information systems [4]. This concept provides a comprehensive perspective on information needs and how they are met. It involves determining who requires information, the type of information needed, its intended purpose, the methods used to locate and assess it and the ways in which these needs are fulfilled.

A significant challenge associated with information-seeking behaviour is information overload. Wilson [5] described information overload as an individual’s perception that the volume of information related to work tasks exceeds what can be managed effectively, creating stress that challenges one’s coping strategies.

In the digital age, where vast amounts of information are constantly accessible, the discussion of digital well-being has become increasingly relevant. Digital well-being is an emerging and rapidly evolving field that examines the impact of digital technologies on individuals’ well-being, including psychological, social and physical aspects [6].

2. Problem statement and research goals

Previous research has explored the correlations between information-seeking behaviour and subjective well-being [7,8] and information overload [9], as well as the correlation between information overload and subjective well-being [10,11]. However, the present study focuses on another term: digital well-being, which has been rarely explored [12,13]. Moreover, this study examines the interplay between information-seeking, information overload and digital well-being in a real-time conflict. By addressing these relationships, this study bridges a gap in the literature, providing valuable insights into how digital practices associate with digital well-being during crises. Thus, the research questions are:

RQ1. To what extent is information-seeking behaviour correlated with individuals’ digital well-being?

RQ2. To what extent is information overload correlated with individuals’ digital well-being?

3. Literature review

3.1. Digital well-being

As digital devices and online platforms become increasingly embedded in daily life, scholars have begun to explore both the benefits, challenges and drawbacks they present. While digital tools can enhance productivity, connectivity and access to information, exposure to vast amounts of digital content has been linked to stress, anxiety, sleep disturbances and decreased life satisfaction [14]. Consequently, digital well-being research seeks to understand how individuals can establish a more balanced interaction with technology, balancing digital engagement with personal well-being [15]. Given the growing concerns about digital overuse, social media influence and screen time effects, the study of digital well-being has gained increasing attention in recent years.

There are several definitions for digital well-being. Burr and Floridi [14] stated that digital well-being is the influence that digital technologies, such as social media, smartphones and artificial intelligence, have on individuals’ well-being. Another researcher Vanden Abeele [6] proposed that digital well-being is the individual’s subjective experience of achieving an optimal balance between the advantages and disadvantages derived from mobile connectivity. Büchi [16] developed a framework to understand and evaluate digital well-being. This framework is designed to explore the complex relationships between individuals’ digital practices, the resulting harms and benefits, and overall well-being. It emphasises that digital media itself is not inherently harmful or beneficial; rather, its impact on well-being is shaped by the socio-technical context in which it is used. The framework identifies three key interdependencies: (a) individuals’ digital practices are influenced by their social environments and technological developments, (b) these practices can lead to both concrete harms and benefits and (c) the balance of these outcomes ultimately affects overall well-being.

Some researchers [12,14] have proposed that spending extended periods online affects digital well-being. Kemppainen and Paananen [17] investigated how users’ favourite digital services, such as the Internet or smartphones, can enhance individuals’ digital well-being. They posit that these services improve users’ digital well-being regarding their psychological, social and cognitive aspects. Psychologically, these services provide retreat, and serenity, and enhance daily tasks. Socially, they foster connectedness and a sense of unity. Cognitively, they promote knowledge, and understanding, and offer inspiration. However, during the COVID-19 pandemic, there has been a significant shift towards digitalisation in various aspects of life, including work, education, medicine and entertainment. This trend has emphasised the significance of digital well-being while simultaneously raising concerns about its potential negative impact [12,18].

Subjective well-being is another term that addresses the individual’s satisfaction with life and emotional experiences [19]. Das et al. [20] suggest that subjective well-being is the evaluation that individuals make about their lives, including the satisfaction levels expressed concerning various dimensions, such as relationships with friends, family and leisure activities.

This study focuses on digital well-being and investigates the association between digital well-being and information-seeking behaviour in times of crisis.

3.2. Information-seeking behaviour

Various models have been developed to explain how information needs emerge and how individuals seek and retrieve information. Among the most notable are Ellis’s [21] behavioural model of information-seeking strategies, Kuhlthau’s [22] model outlining the stages of information-seeking, Wilson’s [23] information-seeking behaviour model and Dervin’s [24] sense-making theory. These models suggest that the motivation to seek information arises from an individual’s recognition of a specific need. In the present study, the authors adopt Wilson’s models [23,25] as the theoretical foundation for their research.

Wilson [23,25] suggested another prominent model. His framework is based on information needs viewed from the user’s perspective. He asserts that these needs stem from humans’ basic needs, including physiological, affective (emotional and psychological) and cognitive aspects associated with learning and training. In addition, he posits that individuals confront numerous barriers in their pursuit of information. To address their information needs, individuals resort to formal or informal sources of information, which may result in either accomplishment or failure in locating pertinent information. When successful, the individual utilises the obtained information, either completely or partially, to satisfy their perceived need. Conversely, there may be cases where the need remains unsatisfied, prompting the individual to repeat the search process [23]. In addition, he suggests that basic information needs can stem from personal, interpersonal, situational and information source characteristics. Wilson proposes that barriers to information retrieval will emerge from these same contextual factors.

In his revised model from 1996, Wilson maintains the fundamental framework established in 1981, where the person in context remains the focus of information needs. However, he introduces the concept of intervening variables, suggesting that variables like psychological, demographic, environmental and source characteristics can either support or prevent information use. Matsveru’s [26] study on information-seeking behaviour among medical doctors, which was based on Wilson’s model, used the following intervening variables: needs, sources, barriers, accessibility and persistence. The current study will similarly use these variables to analyse individuals’ information-seeking behaviour during times of conflict. The following section will concentrate on the intervening variables: needs, sources, barriers, accessibility and persistence and their association with digital well-being.

During crises, people’s information needs are crucial to their digital well-being [12]. They seek information to know how to deal with them [27], how to obtain details about the event, its consequences, government responses, shelter options [28], evacuation procedures [29], rescue and recovery efforts, and the well-being of friends and family [28].

Addressing information sources during crisis situations, Lau et al. [30] reported that exposure to various information sources during the Russo-Ukrainian war has led to notable levels of stress, fatigue and feelings of horror among citizens. As for information-seeking barriers, Hofer et al. [31] found that Internet skills act as a barrier in the relationship between online information-seeking and subjective well-being. As Internet skills improve, the barriers are reduced, and the association between online information-seeking and subjective well-being increases.

With respect to accessibility, some researchers [32] noted that seeking information was vital for staying informed about the COVID-19 pandemic and acquiring precise, prompt and accurate health-related information was linked to lower levels of stress, anxiety and depression. However, it is essential to acknowledge that accessibility to information does not always yield positive outcomes, as demonstrated by public engagement with the ongoing Russo-Ukrainian war that led to notable levels of stress and fatigue [29].

Regarding persistence, Isett et al. [33] revealed that participants’ persistence is a noteworthy variable during information-seeking tasks. They identified a gender difference in search persistence, noting that women are more probably than men to continue searching if the first source does not provide an answer.

In addition, women’s persistence in information-seeking that is characterised by spending more time to resolve uncertainty may contribute positively to their subjective well-being. Based on the literature, researchers formulated the first hypothesis:

H1. There will be an association between individuals’ information-seeking behaviour variables such as needs, sources, barriers, accessibility and persistence and their digital well-being.

3.3. Information overload

Another variable that researchers would like to explore its relation with digital well-being is information overload. Various scholars have dealt with the term Information Overload. Chung et al. [34] consider information overload to be a state in which a person is exposed to an excessive amount of information, which impairs their ability to effectively manage and process it, ultimately leading to increased stress and negative emotions.

Another definition refers to the feeling of being overwhelmed by the amount of information presented, indicating a situation where information exceeds the user’s processing ability, that leads to negative feelings of failure [35]. Shachaf et al. [36] suggested that information overload is considered as the subjective experience of individuals who, while actively searching for information, are faced with overwhelming quantities of information and, as a result, feel unable to handle it effectively.

Information overload can have several negative effects, including frustration, stress and information anxiety [37]. It diminishes the efficiency of information acquisition and hinders effective decision-making while also negatively impacting both physical and mental health. Over time, excessive exposure to information can contribute to more severe problems such as anxiety, depression and social fatigue [10].

Research conducted during crises has shown that the need for information can lead to widespread concerns and information overload. During the Russo-Ukrainian war, citizens faced significant stress and fatigue due to multiple sources and an overwhelming volume of war-related data, coupled with limited time to analyse it, resulting in information overload [1]. Similarly, the abundance of pandemic-related information during the COVID-19 pandemic hindered timely and effective decision-making for protection and increased information overload [38].

Several studies that delved into information overload revealed that regardless of age, occupation or social status, most individuals experience information overload daily [39]. The literature highlights various negative consequences associated with information overload. It is recognised as a significant decision stressor [40] and has implications for psychological well-being, contributes to stress [10] information anxiety [11], depressive symptoms [41], exhaustion and fatigue [42].

Different studies have explored information overload during crises such as COVID-19. According to Yavetz et al.[9], increased media consumption during the pandemic was correlated with participants experiencing information overload. Another study that also took place during COVID-19 (Dreisiebner et al., [43]) revealed that 75% of participants, who used online channels, increased their news consumption which led to overwhelm, reduced information-seeking and active avoidance of specific sources – which are indications of information overload. In addition, research conducted in the early months of the pandemic [44] in the Netherlands suggested a rise in news consumption but also an increase in intentional news avoidance, reflecting individuals’ desire to take breaks from overwhelming information, especially negative emotions associated with it.

Drawing from the literature review, we propose the following hypothesis:

H2. There will be an association between individuals’ information-seeking behaviour variables such as needs, sources, barriers, accessibility and persistence and their information overload.

H3. There will be an association between individuals’ information overload and their digital well-being.

4. Methods

4.1. Data collection and study population

The study employed a quantitative approach and was carried out during October 2023. Prior to participation, all respondents were required to sign an informed consent form and were provided with an explanation of the study’s objectives, including an estimation that the questionnaire would take approximately 15 min to complete. To gather data, a request to fill out the questionnaire was raised in a post in a few Facebook and WhatsApp groups. Researchers used the snowball method, aiming to attract a diverse range of potential volunteers. Respondents engaged with these posts and shared them with additional groups across various social media platforms.

Of the 155 participants, 125 (80.6%) were between 20 and 60 years old, while 30 (19.4%) were 61 or older. Relating to gender, 51 (32.9%) were male, and 104 (67.1%) were female. As for their marital status, 120 (77.4%) were married or in a relationship, while 35 (22.6) defined themselves as having another status. The majority of the participants, 128 (82.6%), were employed, and 27 (17.4%) identified as other. In terms of educational background, 133 (85.8%) were graduates, and 22 (14.2%) did not have an academic education. The majority of the participants, 135 (86.5%), identified as religious, while 21 (13.5%) were non-religious.

4.2. Tools

Researchers used four questionnaires as research instruments (see Supplemental Appendix 1):

The background questionnaire included questions concerning age, gender, marital status, employment, education level and religious affiliation.

The information-seeking behaviour questionnaire was based on Wilson’s model [4] and Matsveru’s [26] questionnaire and consisted of six sections (A to E) with a total of 49 closed questions. The instrument uses a 5-point Likert-type scale (1 = completely disagree; 5 = completely agree). Section A relates to information needs, and its reliability was α = 0.71. Section B addresses information sources, and its reliability was α = 0.60. Section C includes barriers being faced by participants, and its reliability was α = 0.92. Section D focuses on participants’ access to information, and its reliability was α = 0.72. Section E gathered data on participants’ perceptions of their persistence in seeking information, and its reliability was α = 0.74. The reliability of section F: frequency was low (α = 0.55); thus, researchers decided to omit it from the analysis.

The information overload questionnaire was based on Williamson and Eaker’s [45] questionnaire, contains 15 statements and was modified for the current research. The instrument uses a 5-point Likert-type scale (1 = completely disagree; 5 = completely agree), and its reliability was α = 0.95.

The digital well-being questionnaire was based on Gomes et al. [46]. It contains 20 statements and was modified to the current research. The instrument used a 5-point Likert-type scale (1 = completely disagree; 5 = completely agree), and its reliability was α = 0.91.

4.3. Eligibility criteria

Participants in this study were required to meet specific eligibility criteria to ensure the relevance and accuracy of the data collected. Eligible participants had to be Israeli citizens who live in Israel. They were also required to have Hebrew as their mother tongue and be at least 20 years old. These criteria were established to ensure that participants had sufficient familiarity with the local context and language, which was crucial for understanding their information-seeking behaviour during crises. Participants who met these criteria were recruited through online posts on social media.

4.4. Statistical analyses

First, the study variables were examined for outliers, which were defined as values far from the mean by more than 4 standard deviations (Z-scores > |4|) [47]. One outlier was detected and was replaced using the Winsorizing technique [48]. That is, replaced with the nearest non-outlier value. Then, skewness and kurtosis values were calculated to evaluate the shape of the distribution. All variables were found within the normal range (absolute skewness values < 3 and absolute kurtosis values < 10) [49]. Next, correlations between demographic variables and study variables were conducted by χ² tests for independence, Pearson’s correlations and point-biserial correlations. Finally, mediation analysis was conducted using the path analysis model. Models were estimated using the maximum likelihood method, and their fit was assessed using several goodness-of-fit indices [50]. These indices included the χ²/df index, which is considered acceptable when the value is less than 5 or between 1 and 3 for an excellent fit; the Comparative Fit Index (CFI), with adequate values above 0.90 and excellent fit values above 0.95; the Root Mean Square Error of Approximation (RMSEA), with values less than 0.08 for adequate fit and less than 0.06 for excellent fit and the Standardised Root Mean Squared Residual (SRMR), with values less than 0.10 for adequate fit and less than 0.08 for excellent fit. In addition, non-significant paths were omitted to reach the most parsimonious model [51]. To examine the significance of the indirect effects, confidence intervals (CIs) were calculated for each indirect effect based on 5000 bootstrap samples of the data [52]. The indirect effects were deemed significant when the CIs did not include zero [53].

Following the collection of data in a digital spreadsheet, statistical analyses were conducted to test the hypotheses. Specifically, IBM SPSS Statistics version 29 was utilised for descriptive statistics and correlation analyses, while IBM SPSS Amos version 24 was employed for path analysis. The significance level for all analyses was 5% two-tailed. Correlations between the variables were calculated using the Pearson correlations. In addition, a path analysis was conducted aimed at examining the direct and indirect effects of information-seeking behaviour variables on digital well-being via information overload.

5. Findings

To examine the relationship between the dependent variable digital well-being and the independent variables (information behaviour variables, information overload), researchers performed Pearson correlations, which are presented in Table 1.

Table 1.

Means, standard deviations and correlations between the research variables.

Variable		M/%	SD	1	2	3	4	5	6	7	8	9	10	11	12
1.	Age	19.4%	-	-
2.	Gender	67.1%	-	−.004	-
3.	Marital status	77.4%	-	−.13	−.12	-
4.	Employment	82.6%	-	−.46***	−.10	.28***	-
5.	Education	85.8%	-	−.08	.23**	.13	.25**	-
6.	Religiosity level	86.5%	-	.10	.04	.60***	.22**	.11	-
7.	Information needs	2.83	0.66	−.17*	.16*	−.05	.10	−.003	.03	-
8.	Information sources	2.56	0.74	−.16	.05	−.13	.07	.07	−.03	.29***	-
9.	Barriers	1.73	0.57	−.06	−.03	−.15	−.08	−.13	.04	.39***	.21**	-
10.	Accessibility	3.66	0.68	−.11	.11	−.10	.06	.08	−.19*	.09	.26**	−.21*	-
11.	Persistence	3.01	0.76	−.07	.19*	.04	.06	.07	.04	.59***	.22**	.06	.28***	-
12.	Information overload	2.89	0.91	−.32***	.12	.10	.16*	.02	.11	.35***	.29***	.31***	.02	.17*	-
13.	Digital well-being	2.89	0.66	−.32***	.04	−.12	.19*	.04	−.09	.24**	.44***	.07	.31***	.18*	.44***

Note. N = 155. Pearson’s coefficients are presented for continuous variables. For correlations between dichotomous and continuous variables, point-biserial coefficients are presented, and coefficients of χ² tests of independence for correlations between dichotomous variables. The percentages of the dichotomised variables represent the upper score. ^a0 = 20–60, 1 = 61+; ^b0 = Male, 1 = Female; ^c0 = Other, 1 = Married/in a relationship; ^d0 = Other, 1 = Working; ^e0 = Other, 1 = Academic; ^f0 = Non-religious, 1 = Religious.

p < 0.05. **p < 0.01. ***p < 0.001.

Table 1 presents descriptive statistics and associations between background and study variables. As expected, all measures of information-seeking behaviour, except for barriers being faced in obtaining information (barriers), were positively associated with digital well-being. Similarly, all measures of information-seeking behaviour, except for accessibility, were positively associated with information overload. Therefore, the first and the second hypotheses were mostly supported.

Regarding the background variables, most of the associations with the study variables were non-significant. Moreover, only age and employment were associated with information overload and digital well-being, with younger and working participants scoring higher on these measures than older and non-working participants.

5.1. Mediation analysis

Path analysis examined the direct effects of all information-seeking behaviour variables (information needs, information sources, barriers, accessibility and persistence) on digital well-being, including the mediating effect of information overload. Age and employment served as control variables. Non-significant paths were omitted to achieve the most parsimonious model, resulting in the exclusion of persistence and employment from the model.

The paths from information needs, information sources and barriers to information overload (IO) were positive and significant. In addition, positive and significant paths were found from information sources to digital well-being, as well as from accessibility and information overload to digital well-being.

Subsequently, the indirect effects were analysed. Significant indirect effects were found for information needs (β = 0.06, SE = 0.03, bootstrapped 95% confidence interval (CI) = 0.01, 0.13) and barriers (β = 0.06, SE = 0.03, bootstrapped 95% CI: 0.01, 0.11) on digital well-being. However, the indirect effect of information sources was not significant (β = 0.05, SE = 0.03, bootstrapped 95% CI = −0.002, 0.11). These results indicated that higher levels of information needs and barriers led to increased information overload, which in turn predicted higher digital well-being scores. An additional indirect effect of age on digital well-being via information overload was identified (β = −0.08, SE = 0.03, bootstrapped 95% CI: −0.14, −0.03). Overall, the model accounted for approximately 38% of the variance in digital well-being.

Figure 1 presents the path analysis that includes the direct effects of all information-seeking behaviour variables (information needs, information sources, barriers, accessibility and persistence) on digital well-being, as well as the mediating effect of information overload.

Table 2 presents the associations between predictors in path analysis model.

Table 2.

Associations between predictors in path analysis model.

Association
Information needs ↔ Information sources	.29***
Information needs ↔ Barriers	.39***
Information needs ↔ Accessibility	.09
Information sources ↔ Barriers	.21*
Information sources ↔ Accessibility	.26**
Barriers ↔ Accessibility	−.21*
Age^a↔ Information needs	−.17*
Age^a↔ Information sources	−.16
Age^a↔ Barriers	−.06
Age^a↔ Accessibility	−.11

Note. N = 155. ^a0 = 20–60, 1 = 61+.

p < .01. **p < .01. ***p < .001.

6. Discussion

The current study aims to enhance our understanding of citizens’ information-seeking behaviour, information overload and its relationship with their digital well-being in times of crisis. The variable digital well-being is a novel one and was not researched thoroughly so far.

According to Table 1, the mean score for digital well-being (M = 2.89, SD = 0.66) was a moderate one, suggesting that even in crisis times participants did not report experiencing either extremely high or extremely low levels of digital well-being.

Addressing the research hypotheses: H1 posed a correlation between participants’ information-seeking behaviour variables (needs, sources, barriers, accessibility and persistence) and their digital well-being and was partially accepted. All variables of information-seeking behaviour, except barriers, exhibited a positive and significant correlation with digital well-being. Moreover, according to the path analysis, information sources and accessibility were significant and positively predicted participants’ digital well-being.

Disasters often provoke feelings of fear and chaos in individuals, as observed by Freberg et al. [54]. In situations where uncertainty prevails, individuals typically strive to obtain information that can help alleviate their anxiety, cope with the disaster and lessen its impact [27]. According to Jin and Lane [55], risk experience is the strongest predictor of individuals’ online information-seeking behaviour related to COVID-19. Amid the ongoing crisis, the citizens encountered a flood of information and media coverage of the events, echoing the information overload experienced during the 11 September 2001 attack [56]. However, unlike the excessive exposure to visual imagery and message content that heightened stress levels and detrimentally impacted subjective well-being, our study reveals that the overwhelming of information and media coverage contributed to citizens’ digital well-being.

In this sense, our finding emphasises the distinction between subjective well-being and digital well-being, highlighting the unique aspects of digital experience.

Moreover, according to the path analysis findings, participants with more information sources and those with increased accessibility to media sources reported higher levels of digital well-being. In other words, those who actively used various sources of information experienced a sense of control over circumstances that added to their digital well-being throughout the crisis. This finding is similar to Castellacci and Tveito’s [57] findings that suggested that the Internet facilitates systematic and efficient access to information, resulting in enhanced subjective well-being.

Furthermore, findings indicate that higher accessibility to media sources also contributed to individuals’ digital well-being. Researchers assume that the widespread accessibility of digital media sources has played a crucial role in fulfilling individuals’ needs to stay informed and contributed to their digital well-being, as the access to information not only reduced uncertainty but also facilitated a better understanding of the situation, enhancing a sense of control and, in turn, enhancing digital well-being. The increased digital accessibility, as noted by Chan [58], fostered feelings of connectedness, providing emotional support and cultivating a sense of community, all of which positively contributed to participants’ digital well-being in the current study.

Kitkowska et al. [13] explored the factors that may enhance or reduce the value of technology use that may be associated with individuals’ digital well-being. They proposed that individuals often hold certain expectations before engaging with technology, which sometimes may be met, but there might be a disparity at other times. The results of the path analysis in the current research are in accordance with Kitkowska et al.’s [13] study, indicating that the fact that participants’ information-seeking behaviour variables (needs, sources, barriers, accessibility and persistence) match their expectations contributes to their digital well-being.

H2 which assumed a correlation between participants’ information-seeking behaviour variables (needs, sources, barriers, accessibility and persistence) and information overload was also partially supported. Specifically, all variables of information-seeking behaviour, excluding accessibility, exhibited a positive and significant correlation with information overload. This suggests that participants with greater information needs, extensive use of information sources, higher ability to overcome barriers and persistence in seeking information reported higher levels of information overload. Corresponding to this hypothesis, the path analysis indicates that information sources were directly associated with participants’ information overload.

Previous research conducted during crisis times [43,59] has indicated a correlation between information-seeking behaviour and information overload. Amid the current conflict, individuals sought to grasp real-time events by exploring platforms they had not previously used, such as TikTok, Instagram and Telegram, where they encountered uncensored information [3]. We suggest that the widespread use of information sources during the crisis, driven by the need to comprehend real-time events, led to an increase in information overload.

H3 assumed a correlation between participants’ information overload and digital well-being was accepted, as information overload was positively correlated with digital well-being.

The field of digital well-being is relatively new, with limited research available. Most studies focus on the correlation between information overload and subjective well-being. Therefore, our finding is surprising when compared with existing research.

This finding contrasts previous studies that suggested a negative correlation between information overload and subjective well-being both in regular [10,41] and in crisis times [11,60]. In addition, information overload contributes to feelings of uncertainty and anxiety when making high-risk decisions [61].

Furthermore, the results of the path analysis revealed that information overload serves as a mediator between information needs and information barriers and digital well-being. Earlier studies [62,63] focusing on subjective well-being have identified perceived information overload as a mediating variable in the relationship between an individual’s digital practices and their subjective well-being. Other studies posited that seeking information from various sources could promote information overload and consequently reduce subjective well-being [11]. However, our study, which focuses on digital well-being, suggests that information needs and barriers contribute to information overload, leading to elevated levels of digital well-being.

Several researchers [64 –66] maintained that people’s information-seeking behaviour during disasters can be expected to differ from their behaviour in routine situations. This disparity is presented in the present study, where individuals actively seek information to understand evolving circumstances [3]. Consequently, this heightened information-seeking activity leads to increased levels of information overload, which enhances their digital well-being. We posit that during a crisis, participants perceived this information overload as beneficial, as the diverse information obtained during their search met their needs and contributed positively to their digital well-being, providing them with a sense of control and understanding during crisis times.

Addressing the age variable, the path analysis reveals both direct and indirect effects regarding digital well-being. The direct effect between age and digital well-being indicates that younger age is associated with higher levels of digital well-being. This finding reinforces Gomes et al.’s study [46] that found a correlation between young individuals (21-30) and their digital well-being.

The indirect effect between age and digital well-being is mediated by information overload. Findings revealed a negative correlation between age and information overload, meaning that younger age is associated with a higher likelihood of information overload, which, in turn, predicts higher levels of digital well-being. Research indicates that age may impact information-seeking behaviour [67]. Despite this, and although younger individuals are more familiar with information technologies, existing research has highlighted negative outcomes associated with younger age such as technostress [68], information overload [69] and poor sleep quality [70], all of which can significantly relate to younger individuals’ digital well-being. Contrary to previous research, our findings propose that although the younger population experiences higher levels of information overload, their digital well-being is high. This implies that the younger population may experience information overload more effectively, potentially leading to higher levels of digital well-being.

The path analysis model in the present study is consistent with Wilson’s [23] information-seeking model, which served as the conceptual framework for this research. It demonstrates that the intervening variables such as accessibility and information sources, as well as age, are predictors of participants’ digital well-being. Furthermore, the needs and barriers variables identified in Wilson’s 1981 model have predicted digital well-being through the mediation of information overload.

6.1. Research limitations and future research

This study has a few limitations. Data collection took place only during 1 month: October 2023. Moreover, the research population consists of Israelis, limiting the applicability of the interpretations to other cultures. Finally, the participants in the research were exclusively users of Facebook or WhatsApp; thus, the findings may not represent individuals who are not engaged in social media.

For future research, it is advisable to investigate digital well-being in a post-crisis context and assess any differences or similarities compared with the current findings. In addition, we recommend exploring various types of crisis events such as natural disasters hurricanes or earthquakes and their effects on digital well-being. Expanding the scope of crisis types would provide a broader understanding of how different emergencies impact digital well-being. Furthermore, it is recommended to examine digital well-being across different countries. Since this study focused on Israel, future research could explore digital well-being in neighbouring regions that experience conflict or political instability. This investigation may offer valuable insights into how different crisis conditions in other countries are associated with individuals’ digital well-being.

7. Conclusion

The current study introduces an empirical research model that examines the direct and indirect effects of information-seeking behaviour variables on digital well-being, mediated by information overload. This research addresses a gap in the existing literature, which has limited exploration of digital well-being. By focusing on crisis situations, the study highlights the unique importance of digital well-being under challenging circumstances.

The proposed model significantly contributes to the literature by enhancing the understanding of how individuals manage information during crises. It underscores the need for citizens to recognise that their digital practices and news consumption can influence their experience of information overload, impacting their digital well-being. In navigating these challenges, individuals are encouraged to find a personalised balance in handling information overload to optimise their digital well-being.

Moreover, the study suggests that during crises, individuals may reframe their perception of information overload. Rather than solely viewing it as a negative factor, they may reinterpret it as a variable that can potentially enhance their digital well-being. This perspective adds a nuanced understanding to the interplay relationships between information overload and digital well-being in crisis contexts.

Supplemental Material

sj-docx-1-jis-10.1177_01655515251359762 – Supplemental material for Mediation of information overload in the relationship between information-seeking and digital well-being: An exploratory study

Supplemental material, sj-docx-1-jis-10.1177_01655515251359762 for Mediation of information overload in the relationship between information-seeking and digital well-being: An exploratory study by Ayelet Ayalon and Noa Aharony in Journal of Information Science

Footnotes

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

Ayelet Ayalon

Supplemental material

Supplemental material for this article is available online.

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