Development and Validation of the Fomsumerism Scale (FOMS): A New Measure for Fear of Missing Out-Driven Consumerism

Abstract

In an increasingly digital world, shopping and virtual dynamics have converged, leading people to shop online for various reasons. Recently, a new concept has emerged: purchasing driven by Fear of Missing Out (FoMO), leading to the phenomenon of “Fomsumerism,” a fusion of “FoMO” and “consumerism.” In the literature, Fomsumerism is typically measured by separately assessing FoMO and consumerism-related factors, with an assumed correlation between them. Given the absence of an integrated tool, this article describes the development and validation of the FOMS (Fomsumerism) scale, designed to capture the most recognized factors in online consumption behaviors driven by FoMO. The study examined the connections between FOMS scores, FoMO, and Need for Online Social Feedback (NfOSF). In the first study, we included 397 Italian-speaking participants (55.9% female) with an average age of 26.83 years (SD = 9.39). An exploratory factor analysis (EFA) revealed a four-factor structure, identifying the dimensions of self-determination, social comparison, social belongingness, and personal agency. The second study included 418 Italian-speaking participants (63.2% female) with an average age of 29.27 years (SD = 11.57). The four-factor structure demonstrated strong reliability for each subscale and was confirmed through confirmatory factor analysis (CFA). The study found a positive but not fully overlapping link between FoMO and Fomsumerism, indicating that FoMO is a construct distinct from Fomsumerism. Additionally, FOMS scores showed positive associations with NfOSF outcomes, highlighting that as the perceived need for feedback increases, so does the tendency to display purchases on social media to receive it. Overall, the FOMS Scale proved to be a valid and reliable tool for measuring FoMO-induced purchase propensity in a comprehensive and integrated manner.

Keywords

Fomsumerism consumerism FoMO online consumption fomsumer behavior

Introduction

Over the past years, technological progress has rapidly advanced, revolutionizing people’s everyday lives and daily interactions. The digital revolution has given rise to a wide range of technological products and opportunities, with mobile technology and the Internet becoming the most prevalent (French & Shim, 2016). As a result, today’s world is often described as a global hyper-connected ecosystem, characterized by the instant and extremely fast connectivity that exists in, for, and between various digital environments, as well as the deep and extended networks through which people can now interact via the Internet (Hynes, 2024). Thus, in an always-on society, access to new technologies has steadily expanded (Hynes, 2024; Settembre, 2012), profoundly transforming the self and human relationships (Bubaker, 2020) and introducing the possibility of living parts of life online that were once only possible offline. Accessing information, seeking knowledge, communication, and social interactions through social media, along with multimedia content creation and sharing, are among the pivotal examples of this “on-life” transformation (Hargittai & Micheli, 2019), enabling people to keep in contact with others from around the world simultaneously. Furthermore, the current widespread technological access has offered numerous other advantages. Whereas in the past, work took place mainly in offices or other physical environments, today it is possible to work remotely and engage in collaborative activities online through digital communication tools such as email, chat, video conferencing, and cloud computing (Central Statistics Office, 2020, 2023). Similarly, learning has embraced new, diverse channels, expanding beyond the traditional classroom and paper textbooks to include blended learning, online courses, tutorials, and many other educational resources that are easily accessible to both young students and adults anytime, anywhere (Alzahrani & O'Toole, 2017; Singh, 2021). Beyond work and learning, the cost-effectiveness and availability of technology have also brought people closer to a plethora of services, such as entertainment through the film and games industry, home banking and online investing, telemedicine and digital health records for proximity healthcare, and monitoring one’s medical and mental health through specialized apps (Central Statistics Office, 2020, 2023).

The services sector has also witnessed a significant impact of the technological revolution on consumption. Nowadays, the wide range of products available on the Internet makes online shopping more accessible, allowing consumers to easily find what they are looking for and compare prices across different retailers, whenever and wherever they want (Barta et al., 2021; Dubbelink et al., 2021; Shanthi & Desti, 2015). As a result, e-commerce has become increasingly popular in recent years, not only on the dedicated websites of world-famous companies such as Amazon or eBay but also on social networking sites, which are integral to the daily lives of both adults and young people (Barta et al., 2021; Shanthi & Desti, 2015). In this complex digital environment, where people are always connected and observing each other’s life experiences, shopping and virtual dynamics have merged, leading people to shop online for various reasons. In particular, in recent years, the concept of purchasing driven by FoMO (Fear of Missing Out) has gained attention, giving rise to the phenomenon of “Fomsumerism”, a blend of “FoMO” and “consumerism”.

FoMO is one of the most well-known phenomena in Internet use studies (Akbari et al., 2021) and is conceptualized as the apprehension stemming from the inability to keep up with what is happening in the lives of others (Tanhan et al., 2022). When people experience this unpleasant state, they are more likely to place importance on social media and visit them constantly to avoid missing out on socially significant events (Tanhan et al., 2022). Consequently, the role of social media in eliciting feelings and behaviors has also started to affect marketing and consumption choices, increasing consumer behaviors in recent decades (Argan & Tokay-Argan, 2018; Savitri et al., 2022). In this context, Fomsumerism is defined as the consumption occurring on social media due to FoMO, and the person who engages in this behavior is referred to as a “fomsumer” (Argan & Tokay-Argan, 2018). Studies link this phenomenon to individual and social identity dynamics that emerge through the sharing of products and services and in relation to social needs, leading to a tendency to consume as a manifestation of FoMO, driven by the desire to impress others and evoke envy (Argan & Tokay-Argan, 2018; Yaputri et al., 2022). The literature has reached a common agreement on the hypothesis that FoMO is associated with online consumption (Fomsumerism). Despite being a recent field of study, a vast body of research has asserted that consumers who compare what they have with what others have, and realize they are worse off, may make an effort to reduce the possession gap by acquiring products that others have (e.g., Argan & Argan, 2019; Christen & Morgan, 2005; Gurzki & Woisetschläger, 2017; Kang et al., 2019; Zheng et al., 2018). Furthermore, a positive correlation between FoMO and the behavior of millennial consumers is evident (Yaputri et al., 2022). Millennials’ tendency to stay connected and spend money on products significantly contributes to their level of FoMO and the likelihood of subsequent consumption behavior. Additionally, it is confirmed that individuals with a high need for group belonging or a fear of being excluded from the group (FoMO) exhibit a greater tendency toward conformity in consumption (Kang et al., 2019).

According to our study, the needs involved in the Fomsumerism process can be attributed to three psychological constructs: Self-determination, Social Comparison, and Social Belonging. Social networks such as Facebook, Twitter, and Instagram are known to be technological tools for seeking social connections (Ellison et al., 2007; Ye et al., 2021). Experiencing FoMO, or missing out on socially relevant experiences, can induce negative feelings in individuals and lead to dissatisfaction with the human need for relationships and closeness to others, raising concerns related to social exclusion, ostracism, and social anxiety (Beyens et al., 2016; Holte et al., 2022). The motivation driving individuals to act to fulfill the need for relationships is often externally regulated (Self-Determination Theory, Deci & Ryan, 2013) and occurs when an individual is motivated by external forces, such as the possibility of gaining instrumental rewards or avoiding punishments. Fomsumerism manifests as an extrinsic motive that triggers behaviors aimed at achieving social gains, which can alleviate concerns about social exclusion (Kim et al., 2020): consumers are motivated by obtaining social benefits that help reduce their concerns about social exclusion (Kang et al., 2019).

Furthermore, it is commonly known that individuals engage in social comparisons with others to make sense of their own characteristics and abilities (Festinger, 1957). Platforms like Facebook, Instagram, and Twitter facilitate social comparison by constantly providing information about others’ lives, which users then use to infer self-knowledge. The comparisons individuals make online contribute to the development of the perception—often inaccurate—that their own life is less enjoyable than others', partly because individuals are more likely to showcase the best aspects of themselves and their lives to their virtual audience. These comparisons can fuel a desire to emulate others, leading users to mimic their preferences and choices of goods, services, or activities.

Finally, being inherently social animals, humans have a spontaneous tendency to form groups, to feel a sense of belonging to them, and to distinguish their ingroups from outgroups (Tajfel & Turner, 1986). The need for belongingness can sometimes drive individuals to conform to attitudes and behaviors that may not align with their own values or even contradict them. This is the case with behaviors driven by Fomsumerism. Constant exposure to content shared by users negatively impacts an individual’s feelings about events they did not participate in and contributes to the development of uncertainty about their social belongingness (Rifkin et al., 2015). Seeing what other people and celebrities are doing in the social media world motivates consumers to develop FoMO not only for events like parties or concerts but also in terms of products and lifestyle choices (Dursun et al., 2023; Kim et al., 2020; McDermott, 2017), thus contributing to the rise of Fomsumerism. The fear of social exclusion and ostracism can motivate individuals to align their behavior with that of the group (Abel et al., 2016; Holte et al., 2022). This fear of being overlooked reflects a desire to maintain relevance not only among close friends but also within the broader social environment.

Studies in this area tend to measure the phenomenon of Fomsumerism by assessing Fear of Missing Out and consumerism-related factors separately, assuming a relationship between them. However, an integrated and validated scale is actually lacking. To measure FoMO, researchers often use scales like the Fear of Missing Out Scale (FoMOs; Alt & Boniel-Nissim, 2018), which focuses on the subjective experience of FoMO and includes elements such as anxiety, depression, and fear of missing out. Another example is the FoMO Measurement Scale by Golp and colleagues (2016), which analyzes FoMO anxiety in daily life situations, online activities, and involvement in social events; or the scale by Przybylski et al., 2013a, 2013b) that consists of ten statements related to the fear of missing out on social events or opportunities. Other studies, to relate FoMO to consumerism, start from the assumption that FoMO is mainly formed by two dimensions: desire to belong and anxiety of isolation. Based on this, custom questionnaires are developed to measure these variables and relate them to consumption (Kang et al., 2019). In general, most of the items used to measure FoMO in relation to shopping conformity have been specifically created based on previous research in this area. Additionally, different aspects of consumerism have been assessed in connection using established scales such as the Consumers’ Need For Uniqueness (CNFU; Tian et al., 2001), which assesses consumers’ creative choices and tendencies to avoid similarity; the Clark Scale (Clark, 2006), which measures consumers’ independence; or the Conformity to Consumption Norms Scale (Bearden et al., 1989), which investigates consumers’ susceptibility to interpersonal influence and the extent to which individuals conform to the consumption patterns of their peers. Moreover, other studies have also considered aspects related to the Compulsive Buying Scale (Edwards, 1993) for their association with FoMO.

Overall, current measurements of Fomsumerism treat consumerism as a manifestation associated with FoMO, but integrated conceptualizations and instruments are still lacking. Indeed, there is currently no validated scale that measures the construct of FoMO in relation to consumerism. Similarly, the scales used to measure consumerism do not take the FoMO variable into account, except in a cross-sectional manner. This indicates a limitation in the assessment of the construct, as consumerism and FoMO should not be considered separately. Thus, the development of a new scale for Fomsumerism is particularly significant in light of both the literature and current e-commerce trends. Indeed, our new scale tends to fill a critical gap in the literature by providing a tool capable of capturing the multidimensional phenomenon of Fomsumerism in an integrated and comprehensive way. Moreover, the need for such a measure is also underlined by recent data on online purchasing and consumption trends. Indeed, some reports show that in 2024 global online product purchases grew by +8% compared to 2023 (Osservatori Digital Innovation del Politecnico di Milano, 2024). In Italy, in particular, some shopping areas saw even stronger increases in 2024 compared to 2023, such as fashion (+25.7%), luxury goods (+21.4%), home furnishings (+18%), household products (+16.3%) and electronics (+11.4%) (We Are Social & Meltwater, 2024). Globally, e-commerce and online shopping is now the fourth most common online activity after chatting, using social networks and search engines, and is used by three out of four Internet users on a monthly basis (We Are Social & Meltwater, 2024). As part of this, global retail e-commerce sales reached $5.8 trillion in 2023, with forecasts predicting that this phenomenon will increase by 39% to over $8 trillion by 2027 (Statista, 2024). These data therefore support the need and current growing importance of understanding Fomsumerism from a psychological perspective, reinforcing the relevance of this new scale in helping to address the complexities of online Fomsumer behaviour in today’s digital landscape. Here, the scale is also expected to help unravel the well-being dynamics associated with FoMO-driven online shopping, while paving the way for interdisciplinary advances in policy-making, educational initiatives and targeted interventions.

Aim of the study and hypotheses development

Considering the absence in the literature of a single, integrated tool for evaluating the construct of Fomsumerism, the aim of our study is to present and validate, both internally and externally, a new instrument for measuring Fomsumerism that can effectively capture the factors most recognized as important in online consumption behavior due to FoMO. To achieve this aim, the authors conducted two studies: the first aimed at examining the dimensionality of the Fomsumerism construct and refining the scale, while the second was conducted to confirm the identified factorial structure and provide evidence of external validity. For the purpose of establishing external validity, the authors developed two main hypotheses regarding the relationships the FOMS Scale would have with the identified variables.

In this complex digital environment, shopping and virtual dynamics have converged, leading people to increasingly make purchases online for various reasons. Recently, the concept of ‘purchasing due to FoMO' has been proposed, giving rise to the phenomenon of ‘Fomsumerism,' a term coined from the fusion of ‘FoMO' and ‘consumerism,' which integrates the fear of missing out (FoMO) with consumerism. Currently, there is no unified scale in the literature that measures both constructs together; they are often assessed separately through measures of consumerism and, tangentially, FoMO. Given the integration of these two constructs into the concept of Fomsumerism and the recognition of a relationship between them, though as separate entities, we hypothesize:

A positive and relatively broad relationship between Fomsumerism and FoMO.

Fomsumerism appears to stem from the need to build and manage a social identity, as well as a desire to belong to a community. The stronger the perceived need for feedback to feel connected to a community, the greater the tendency to make purchases and display them on social media to receive validation. Based on this we expect:

Higher FOMS scores correspond to higher NfOSF scores.

Methods

In the first step, the initial version of the new FOMS was piloted on 30 participants of different ages and education levels, in order to investigate if there could be any understanding problems in the formulation of the items. The pilot sample consisted of 14 women and 16 men, with an age range of 18–39 years. 15% of the participants had a lower secondary education, 40% had a high school diploma, 25% had a Bachelor’s degree, and 20% had a Master’s degree.

After this phase, the research group proceeded to define an adequate sample size based on the type of analyses that were intended to be performed (i.e., confirmatory factor and correlation analyses). In this regard, the analysis with the highest requirements in terms of observations was used to set the threshold for the sample size to be reached.

We conducted two studies for the Exploratory Factor Analysis (EFA) in Study 1 and the Confirmatory Factor Analysis (CFA) in Study 2

In Study 1 for EFA, since we expected 4 factors to represent the 25 items of the scale we developed, a sample size slightly lower than 349 would be enough for conducting exploratory factor analysis even assuming quite-low factor loadings (λ = 0.4) based on de Winter and colleagues’ work (de Winter et al., 2009). Since the pilot test did not reveal any particular problem or need for adjustments, the FOMS was administered to 397 participants, recruited through a voluntary census with dedicated messages on the web, social media, and mailing lists via snowball sampling method.

After the EFA, the scale was further refined in terms of items, reaching 16 items.

In Study 2 for CFA, we followed the advice of having at least a 10:1 ratio of participants to items (Comrey, 1988), and a sample size greater than 200 was regarded “adequate” to properly complete the confirmatory factor analysis (Comrey & Lee, 1992; Kline, 2023). Furthermore, we used G*Power (Faul et al., 2007) to do a power analysis for Pearson’s correlation. According to the power analysis, a sample size of 273 people would be required at the very least to achieve a statistical power of 0.80 while being able to capture effects less than typical (r = 0.15). Since the pilot test did not reveal any particular problems or need for adjustments, the FOMS was administered to 418 participants, recruited through a voluntary census with dedicated messages on the web, social media, and mailing lists via snowball sampling method. Given that 418 surpassed the 273 threshold indicated by power analysis, we deemed our sample size as adequate.

After the CFA, the scale was further refined in terms of items, reaching 14 items.

In Study 1, the final sample consisted of 55.9% cisgender women, 42.8% cisgender men, 0.3% transgender women, and 0.3%, transgender men, with a mean age of 26.83 (sd = 9.39).

A part of the sample has a high school educational level (33.8%) while 36.8% have a Bachelor’s degree and 20.9% have a Master’s degree.

In Study 2, the final sample consisted of 63.2% cisgender women, 34% cisgender men, 1.7% transgender women, and 1.2%, transgender men, with a mean age of 29.27 (sd = 11.57).

We conducted the data collection process in accordance with Italian data protection regulations (Legislative Decree DL - 101/2018), EU regulations (2016/679), and APA guidelines.

Procedures

For the development of the FOMS, the authors started by analyzing available and validated scales used to capture the concept of Fomsumerism, identifying the lack of an integrated and valid Fomsumerism Scale, and thus the need to provide a new one.

The ad hoc formulation of the items of the new FOMS took place through fifteen Focus Groups, held between March and May 2020 through the remote Zoom platform. The focus groups were coordinated by the authors of this paper and involved the participation of two groups of six members each, whose aim was to create items that would investigate Fomsumerism as accurately as possible, thanks to brainstorming and discussion activities with all the participants involved in the process, in order to reach an agreement.

Each selected participant had a postDoc in Psychology. At the beginning of each session, the conductor of the Focus Groups initiated a PowerPoint presentation in which the theoretical underpinnings of the construct under examination, as well as the basics relating to construct validity, aesthetic validity, and item sensitivity were presented.

Each Focus Group focused on the creation of a single item, starting with the formulation of a protoitem, the precursor to the actual item that incorporates the theoretical notions of the construct indicating the relationship between two variables highlighted by certain studies in the literature. An example of a protoitem is: ‘Those who have the desire to build an identity (social and individual) through social networks experience higher levels of Fomsumerism’ from which the item (in the survey) was derived: ‘Through my online purchases I am also able to develop my identity, to be myself’.

Each focus group had two different phases: in the first brainstorming phase, the subjects were asked to verbally express their opinions on the presented protoitem and to create an item reflecting it. In the second phase of the focus group, participants were asked to identify which of the items produced in the previous phase were the most indicative of the relationship considered. For each proto-item, two versions of the item were created: the first included a non-reasoned, automatic and axiomatic item based on the heuristics and the second version referred to a more rational and explicit item. In the subsequent Focus Group sessions, all contributors to this research took part and the items that made up the final survey were decided upon. Test participants were asked their degree of agreement for each item on a 5-point Likert scale (from 1 = ‘Strongly disagree’ to 5 = ‘Totally agree’).

For data collection, a survey was realized via the Google Forms platform, from which a link to the questionnaire was provided to the participants. To complete the survey, they needed to speak and understand the Italian language and be at least 14 years of age. They were asked to consent to the processing of their data completely anonymously and in full respect of privacy.

Measures

Participants first responded to demographic questions on age, gender, and educational qualification, and additionally, the following measures were used to achieve the objectives of the study.

Our new formulation of the Fomsumerism Scale (FOMS) cointans 25 items assessed on a 5-point Likert scale (from 1 = “Strongly disagree” to 5 = “Totally agree”). The full formulation is shown in Table 1 in the Appendix.

The Fear of Missing Out (FoMO) phenomenon was investigated through the Fear of Missing Out Scale, developed by Przybylski et al., 2013a, 2013b). The study used the validated Italian version of the scale (Casale & Fioravanti, 2020). The FoMO scale contains 10 items assessed on a 5-point Likert scale (from 1 = “not at all true” to 5 = “extremely true”). Examples of items are: “I am afraid that others have more rewarding/beautiful experiences than me”, “I get annoyed when I miss an opportunity to meet my friends” and “When I do something nice, it is important for me to share it online (e.g., by updating my status, posting photos)”. The structure of the scale comprises two factorial dimensions: the first includes 4 items related to the fear that others will have more rewarding experiences or that they will have fun without the subject; the other comprises 6 items assessing the individual’s need to have control over what is happening within their social network. The Cronbach’s Alpha coefficients of the two factors have high values: α = 0.79 for Factor 1 and α = 0.73 for Factor 2.

Individuals’ need for online social feedback was assessed using the Need for Online Social Feedback (NfOSF; Duradoni et al., 2023) scale. The NfOSF consists of 5 items that capture two factors: Factor 1, which groups elements related to the desire to receive positive feedback (e.g. “I am happy that people see my content online”) and Factor 2, which is linked to a greater desire for fame (e.g. “I would like my online content to go viral”). The full formulation is shown in Table 2 in the Appendix. The scale uses a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). For the first dimension of the scale (i.e., Desire), the minimum score is 3 and the maximum is 15. Therefore, a higher score in the subscale implies higher scores of the need for positive feedback. On the other hand, regarding the second dimension of the scale (i.e. Fame), the minimum score is 2 and the maximum is 10. Furthermore, a higher score in the subscale implies higher scores of the desire for online fame. The reliability analysis of the NfOSF bifactor model was assessed with McDonald’s omega and both factors showed optimal reliability (F1 ω = 0.84; F2 ω = 0.90).

Results

Study 1: Exploratory Factor Analysis (EFA)

To investigate the factor structure using Exploratory Factor Analysis (EFA), we employed the Principal Axis Factoring extraction method with Promax rotation. The determination of the number of components to extract was guided by the examination of the scree plot (Cattell, 1966) and the application of the Kaiser criterion (i.e., retaining all factors with eigenvalues greater than one) (Kaiser, 1960). Through an iterative process, items with factor loadings below .50 (Ferguson & Cox, 1993) or those exhibiting cross-loadings were systematically removed, reducing the initial item pool from 25 to 16 items. Specifically, item 20 was removed as it was deemed overly generic, with a low extraction value of 0.27. Similarly, item 24 was excluded due to a low extraction value of 0.24. Item 19 was eliminated because of cross-loading issues, while items 6, 7, 21, 22, and 15 were removed due to consistently low factor loadings. Finally, item 9 was excluded for the same reason. Additionally, the results of Bartlett’s Sphericity Test supported the appropriateness of factor analysis for our dataset. Both the initial EFA (χ² = 4594.36, p < .001) and the final EFA (χ² = 2613.27, p < .001) confirmed that the correlation matrix was significantly different from an identity matrix, meeting the necessary prerequisites for factor analysis. This refined item pool accounted for 64.15% of the variance across four factors (Table 1).

Table 1.

EFA Results for FOMS Structure and Factor Loadings.

Item number	F1_New-Self determination	F2_New-Social comparison	F3_New-Social belonginess	F4_New-Personal agency
25	0.88
17	0.72
23	0.66
18	0.66
16	0.52
12		0.84
14		0.74
13		0.73
11		0.53
10			0.70
4			0.70
5			0.68
8			0.54
1				0.67
3				0.66
2				0.65
Eigenvalues	6.04	1.80	1.43	1.01
Explained total variance	37.73%	11.24%	8.91%	6.27%
Cumulative total variance	64.15%

Study 2: Confirmatory Factor Analysis (CFA)

To validate and compare the factorial structures identified previously, Confirmatory Factor Analysis (CFA) was conducted on the second sample (N = 418). Maximum Likelihood Estimation (MLE) was utilized to estimate the model parameters. The model fit was assessed using multiple goodness-of-fit indices, including the chi-square to degrees of freedom ratio (χ²/df; Jöreskog, 1969), the Tucker-Lewis Index (TLI; Tucker & Lewis, 1973), the Comparative Fit Index (CFI; Bentler, 1990), the Standardized Root Mean Square Residual (SRMR; Bentler, 1995), and the Root Mean Square Error of Approximation (RMSEA; Browne & Cudeck, 1992). For the TLI and CFI, values exceeding .90 are deemed acceptable, with values above .95 indicating an optimal fit. Regarding the RMSEA, values below .08 are considered acceptable, while values approaching .06 signify an optimal fit. The SRMR should be below .08 to indicate a good fit (Byrne, 2013; Hu & Bentler, 1999).

First, we attempted to precisely replicate the structure identified in Study 1, which consisted of 4 factors defined by 16 items. As shown in Table 2, the results indicated a suboptimal but potentially acceptable fit. Consequently, we analyzed the modification indices, which revealed that item 4 exhibited cross-loading with two other factors. The research team deemed this unacceptable and removed the item, resulting in improved fit indices across the board. However, the model still displayed a suboptimal TLI value. Subsequently, further analysis of the modification indices indicated that the error term for item 16 was correlated with the error term of another item not associated with the same factor. This led to the removal of item 16 as well. After this removal, all considered fit indices indicated an excellent fit of the model to the data. Eventually, all factor loadings were statistically significant and higher than 0.50 (Figure 1).

Table 2.

CFA Results of the 3 Models Tested.

Models	χ²/df	TLI	CFI	RMSEA	SRMR
Model 1	3.36	0.92	0.93	0.08	0.05
Model 2 (item 4 removed)	2.90	0.94	0.95	0.07	0.04
Model 3 (items 16 and 4 removed)	1.95	0.97	0.98	0.05	0.03

Descriptive Statistics

At this stage, supported by the factorial analysis, we computed the total scores for the dimensions of the FOMS scale, along with the measures to test external validity. The descriptive statistics of the scores are presented in Table 3, along with their skewness and kurtosis values, which confirm the normal distribution of the scores as they fall within the range of −1 to +1. Given the satisfaction of statistical assumptions for parametric inferential analyses, we proceeded with Pearson correlation as planned.

Table 3.

Descriptive Statistics of the Collected Variables.

Variable	Min-Max	M	s.d.	Skew.	Kurt.
F1_New-self determination	4–20	7.96	3.246	0.785	0.594
F2_New-social comparison	4–20	10.10	3.282	0.090	−0.340
F3_New-social belongingness	3–15	6.67	2.555	0.280	−0.425
F4_New-personal agency	3–15	10.43	2.118	−0.189	0.228
FoMO_Fear	4–20	8.29	3.610	0.790	0.199
FoMO_Control	6–27	14.96	4.216	0.057	−0.281
FoMO_Total	10–45	23.25	7.113	0.407	−0.002
I feel I’ve achieved a/an … reputation on social media.	1–5	2.94	1.008	−0.158	−0.066
NOSF-feedback desire	3–15	9.90	3.055	−0.412	−0.482
NOSF-fame desire	2–10	5.08	2.579	0.438	−0.931
Age	15–86	29.27	11.571	1.748	2.495

Note. M = mean/average; s.d. = standard deviation; Skew. = skewness; Kurt. = Kurtosis; FoMO = Fear of Missing Out; NOSF = Need for Online Social Feedback.

Convergent Validity and Discriminant Validity

Regarding the relationship between the four dimensions of the FOMS scale and FoMO (Table 4), we observe typical (around 0.20) to relatively large (above 0.30) correlations (Gignac & Szodorai, 2016) for three dimensions (i.e., self-determination, social comparison, and social belonging) with the two dimensions of the Italian version of FoMO. Conversely, the personal agency dimension appears to be independent of FoMO. In relation to the desire for online feedback on social media, all four dimensions of the FOMS scale show some degree of correlation with the “Feedback Desire” dimension. However, the strongest correlations are with self-determination and social comparison. The “Fame” dimension, on the other hand, is associated with only three dimensions of the FOMS, again exhibiting typical to relatively large correlations.

Table 4.

Correlation Matrix of FOMS With External Validity Measures.

	F1_New-Self determination	F2_New-Social comparison	F3_New-Social belonginess	F4_New-Personal agency
FoMO_Fear	0.274^***	0.353^***	0.155^***	0.078
FoMO_Control	0.329^***	0.398^***	0.252^***	0.065
NOSF-feedback desire	0.229^***	0.303^***	0.144^**	0.113^*
NOSF-fame desire	0.302^***	0.293^***	0.176^***	0.072

Note. FoMO = Fear of Missing Out; NOSF = Need for Online Social Feedback; *** = p < 0.001; ** = p < 0.01; * = p < 0.05.

Internal Reliability

The reliability of the FOMS four-factor model was evaluated using McDonald’s omega, aligning with the prevailing consensus in psychometric literature that Cronbach’s alpha is often unsuitable (Dunn et al., 2014; Hayes & Coutts, 2020; Watkins, 2017). All four factors showed at least an acceptable reliability (F1 ω = 0.85; F2 ω = 0.79; F3 ω = 0.87; F4 ω = 0.70). We also tested the composite reliability, which is based on the congeneric model that does not require equivalent factor loadings across items (F1 CR = 0.91; F2 CR = 0.84; F3 CR = 0.91; F4 CR = 0.74). For each factor, the composite reliability exceeded the recommended threshold of 0.7 (Cheung et al., 2024).

Discussion

For several years now, the use of new technologies has been one of the fundamental prerequisites for working, performing basic citizenship functions (e.g., online voting), finding news, spending leisure time watching movies or series, playing games, or staying in touch with others almost anywhere in the world (Central Statistics Office, 2020; Hargittai & Micheli, 2019). The service sector has also seen a significant impact of the technological revolution on consumption. Nowadays, the wide range of products available on the Internet facilitates online shopping, allowing consumers to easily find what they are looking for and compare prices across different retailers, whenever and wherever they want (Barta et al., 2021; Shanthi & Desti, 2015). In recent years, e-commerce has become increasingly popular, not only on the dedicated websites of world-renowned companies like Amazon or eBay but also on social networking sites, which are integral to the daily lives of adults and young people (Barta et al., 2021; Shanthi & Desti, 2015). In this complex digital environment, where people are constantly connected and observing others’ life experiences, shopping and virtual dynamics have intersected, leading people to shop online for various reasons. Recently, the concept of purchasing driven by FoMO has emerged, giving rise to the phenomenon of “Fomsumerism”, a blend of “FoMO” and “consumerism,” which integrates these two concepts (Argan & Tokay-Argan, 2018).

The literature reveals several gaps in measuring this construct in relation to consumerism, as existing questionnaires measure consumerism and FoMO separately, but a unified and integrated tool is still lacking. Thus, the need to construct and validate a scale to measure Fomsumerism (the FOMS Scale) has been highlighted. To develop the FOMS Scale, proto-items were initially formulated and analyzed. Subsequently, two studies refined a structure of 14 items representing four underlying factors of Fomsumerism: social comparison, social belongingness, personal agency, and self-determination, each demonstrating strong reliability. Regarding the external validity of the scale, FoMO emerges as a construct separate from Fomsumerism. This separation does not imply total independence but rather suggests an “oblique” relationship, indicating some form of connection between them while maintaining their distinct identities. In our study, we hypothesized a positive but not completely overlapping relationship between FoMO and Fomsumerism. Moreover, our study suggests that an explanation of Fomsumerism can also be provided by referring to the Need for Online Social Feedback (NfOSF) (Duradoni et al., 2023). Based on the identified dimensions, Fomsumerism may stem from the need to construct and manage a social identity, as well as to belong to a community, where feedback serves as a form of validation and inclusion. Indeed, the greater the perceived need for feedback, the more likely individuals are to showcase their purchases on social media to receive it. This result confirms our second hypothesis (H2): higher FOMS scores correspond to higher NfOSF scores. Fomsumerism manifests as a motive that triggers behaviors aimed at obtaining social gains, which can alleviate concerns about social exclusion (Kim et al., 2020). As social beings, people have a natural tendency to form groups, feel a sense of belonging, and distinguish their ingroup from outgroups (Tajfel & Turner, 1986). The need for belonging can sometimes drive individuals to conform to attitudes and behaviors that may not align with or even contradict their own values. This is the case with behaviors driven by Fomsumerism. Constant exposure to content shared by others can negatively impact an individual’s feelings about events they did not participate in, contributing to uncertainty about their social belonging (Rifkin et al., 2015). Receiving feedback from one’s community helps reaffirm their sense of inclusion.

Implications and Future Perspectives

The use of this scale can provide a better understanding and a more comprehensive analysis of Fomsumerism. It allows for more precise measurements compared to considering FoMO as merely a cross-sectional aspect of consumerism. In the literature, some studies have examined the relationship between FoMO, planned/unplanned purchases, and post-purchase regret (Flecha Ortiz et al., 2024; Ögel, 2022), but have used separate questionnaires to measure these variables. Instead, it would be beneficial to analyze the relationship between these factors using the FOMS Scale, also examining its long-term effects on well-being.

Furthermore, the validity of the FOMS Scale could be tested in different cultural contexts. Some studies have described FoMO as a culturally sensitive phenomenon (Karimkhan & Chapa, 2021), suggesting that it might influence Fomsumerism differently depending on whether a culture is more individualistic or collectivistic. This indicates a potential need to culturally adapt the FOMS Scale. An interesting area for future research could explore the connection between cultural narcissism, FoMO, and consumerism. Cultural narcissism, characterized by a society that promotes narcissistic behaviors on a large scale, may increase dependence on external approval and fear of social exclusion, both of which can lead to more compulsive buying (Kuss & Griffiths, 2017). The FOMS Scale could be an effective tool for assessing how cultural narcissism contributes to greater vulnerability to FoMO-driven consumerism, offering a deeper understanding of these interconnected dynamics.

In line with Lewin’s field theory (1943), and beyond cultural differences, future studies could also explore the relationship between the FOMS Scale and individual differences (e.g., personality traits) to identify potential predispositions and tailor interventions. Following Lewin, longitudinal studies could be conducted using the FOMS Scale to investigate the evolution of Fomsumerism across different life stages, as well as in response to major environmental and social changes. For example, the scale could be useful in exploring how Fomsumerism contributes to increased waste and environmental impact due to frequent product purchases, with the possibility of developing strategies to mitigate these effects (Namakula, 2021; Schmitt et al., 2022). Indeed, the FOMS Scale could serve as a solid foundation for creating preventive, educational, or therapeutic interventions aimed at reducing Fomsumerism, thereby enhancing well-being and minimizing the negative consequences of overconsumption.

Moreover, future research could explore the potential integration of findings from the FOMS Scale with artificial intelligence (AI) (Peruchini et al., 2024). Data collected with this scale could be utilized in AI-based systems to help professionals and researchers identify specific patterns in purchasing behavior and emotional dynamics, enabling highly tailored psychological interventions while fully respecting ethical and privacy concerns.

Limitations

Our results are not without limitations, including non-randomized sampling and self-selection bias, which may limit the generalizability of the findings. Additionally, the average age of our sample was 26.83 years in Study 1 and 29.27 years in Study 2, suggesting that the results may not generalize well to other age groups, such as adolescents or older adults. Moreover, the presence of different ethnic backgrounds among the participants was not examined, so future studies should consider the impact of cultural differences on FOMS scores. Future research should test the FOMS scale in other countries and with diverse populations to address these limitations and further explore discriminant validity. This is because the analysis in these studies was limited to examining the relationships between the measures selected for assessing convergent validity and other FOMS dimensions, which were assumed to be less correlated.

Conclusion

In conclusion, despite its limitations, the FOMS Scale proves to be a valid and reliable tool for measuring FoMO-induced purchase propensity. It comprises four factors: self-determination, social comparison, social belongingness, and personal agency. The FOMS Scale could enhance our understanding of the psychological dynamics underlying online-promoted behaviors, providing deeper insights into how Fomsumerism may affect stress, anxiety, and depression. It could also be used to explore the relationship between this construct and cultural narcissism, and how it intersects with other important research areas, such as environmental sustainability.

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

Mirko Duradoni

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Appendix

Table 1.

Fomsumerism Scale (FOMS) First Complete Formulation.

1. IT: Quando acquisto on-line mi sento più libero di effettuare le mie scelte.
ENG: When I shop online, I feel freer to make my own choices.

2. IT: Comprare online mi permette di scegliere il prodotto migliore.
ENG: Shopping online allows me to choose the best product.

3. IT: Comprare online mi permette di essere più indipendente.
ENG: Shopping online allows me to be more independent.

4. IT: Comprare sulle piattaforme online che usano gli altri mi fa sentire meno isolato.
ENG: Shopping on the online platforms that others use makes me feel less isolated.

5. IT: Mi piace comprare online anche per la possibilità che mi da di condividere i miei acquisti con i miei amici.
ENG: I like shopping online also because of the opportunity it gives me to share my purchases with my friends.

6. IT: Essendo molto attivo sui social mi viene naturale comprare on-line.
ENG: Being very active on social media, it comes naturally to me to shop online.

7. IT: Tendo ad acquistare online ciò che le persone che ammiro consigliano.
ENG: I tend to buy online what people I admire recommend.

8. IT: Comprare online mi fa anche sentire parte di una comunità.
ENG: Shopping online also makes me feel part of a community.

9. IT: Quando compro online spesso mi piace condividere i miei acquisti con i membri della mia comunità.
ENG: When I shop online, I often like to share my purchases with members of my community.

10. IT: Comprare online in qualche modo mi fa sentire meno isolato dalla mia comunità.
ENG: Shopping online somehow makes me feel less isolated from my community.

11. IT: Quando compro on-line spesso sono influenzato dalla mia comunità.
ENG: When I shop online I am often influenced by my community.

12. IT: Talvolta le cose che compro on-line mi fanno sentire più “cool” agli occhi dei miei amici.
ENG: Sometimes the things I buy online make me feel “cooler” in the eyes of my friends.

13. IT: Tramite ciò che acquisto on-line riesco anche a sviluppare la mia identità ed essere me stessa/o.
ENG: Through what I buy online I am also able to develop my identity and be myself.

14. IT: Comprare online mi permette di essere una persona più piacevole agli occhi degli altri.
ENG: Shopping online allows me to be a more pleasant person in the eyes of others.

15. IT: Spesso attraverso gli acquisti on-line riesco ad acquisire una reputazione che mi soddisfi.
ENG: Often, through online shopping, I manage to build a reputation that satisfies me.

16. IT: Talvolta compro on-line per avere un po’ più di visibilità nella mia comunità.
ENG: Sometimes I shop online to get a little more visibility in my community.

17. IT: Comprare on-line mi permette anche di condividere una parte della mia vita con gli altri.
ENG: Shopping online also allows me to share a part of my life with others.

18. IT: La soddisfazione ed il piacere derivante dal frequentare i social dipende anche dai miei acquisti on-line.
ENG: The satisfaction and pleasure I get from being on social media also depend on my online purchases.

19. IT: Attraverso i miei acquisti on-line riesco a farmi notare di più dagli altri.
ENG: Through my online purchases I am able to get others to notice me more.

20. IT: Quando compro on-line è solitamente importante l’approvazione dei miei amici.
ENG: When I buy online, the approval of my friends is usually important.

21. IT: Comprare on-line per me è uno “stile di vita”.
ENG: Shopping online is a “lifestyle” for me.

22. IT: Comprare on-line talvolta mi permette di avere beni esclusivi che altri non hanno.
ENG: Shopping online sometimes allows me to have exclusive goods that others do not have.

23. IT: Appena acquisto on-line, sento subito il bisogno di condividere il mio acquisto sui social.
ENG: As soon as I shop online, I immediately feel the need to share my purchase on social media.

24. IT: Acquistare in un negozio di solito mi mette maggiormente a disagio rispetto ad acquistare on-line.
ENG: Shopping in a store usually makes me feel more uncomfortable than shopping online.

25. IT: Acquistare on-line mi da anche la possibilità di interagire più sui social.
ENG: Buying online also gives me the opportunity to interact more on social media.

Note. The items in the Italian version are validated, while the English version is not currently validated.

Table 2.

Need for Online Social Feedback Scale (NfOSF; Duradoni et al., 2023).NfOSF - Factor 1: Feedback Desire

1. ENG: I’m pleased that people view my online contents. IT: Ho piacere che le persone visualizzano i miei contenuti online.

2. ENG: I feel satisfaction when I receive positive feedback (eg the likes) on my contents.
IT: Provo soddisfazione quando ricevo feedback positivi (ad esempio like) ai miei contenuti.

3. ENG: For me, it’s important to receive appreciation for my online contents.
IT: Ricevere apprezzamenti per i miei contenuti online è importante per me.

NfOSF - factor 2: Fame desire

4. ENG: I would like my online contents to go viral.
IT: Mi piacerebbe che i miei contenuti diventassero virali.

5. ENG: I would like to have a large online following.
IT: Mi piacerebbe avere un largo seguito online.

Note. For the NfOSF Scale, theoretical scores for the first dimension (i.e. Desire) range from a minimum of 3 to a maximum of 15, and for the second dimension (i.e. Fame) from a minimum of 2 to a maximum of 10. The total score of each factor is given by the simple sum of the items belonging to each factor.

Author Biographies

Mirko Duradoni obtained the Ph.D. title in Information Engineering in 2020 and he holds a master's degree in psychology from the Department of Education and Psychology, University of Florence, Italy. He is currently a member of the Virtual Human Dynamics Laboratory at the University of Florence, a contract professor in Psychology of groups (2019-2022), and a teaching fellow in environmental psychology (2021-2022). He is particularly skilled in psychological dimensions assessment, research methodology, and data analysis. He taught psychometrics in the health sector (2016-2019, University of Pisa) and was enrolled for his skills in three EU-level projects, namely InSPIRES (ID: 741677), Erasmus+ Restore (IT02-KA204-063248), and “PHOENIX: The rise of citizens’ voices for a Greener Europe” (ID: 101037328). Finally, he worked in the last years, together with researchers from the Department of Industrial Engineering (DIEF-UNIFI), developing knowledge and expertise in how ICTs and other technologies could promote and integrate human well-being (e.g., robot-based psychotherapy, persuasive technologies, mHealth; Virtual Reality interventions), as well as support people during the Covid-19 pandemic (e.g., contact tracing systems).

Franca Paola Severino obtained master's degree in Psychology of Life Cycle and Contexts and, since April 2024, has been a research grant with the University of Florence, at the Department of Biomedical Sciences at the Endocrinology Outpatient Clinics of the Careggi University Hospital. In December 2023, she qualified to practice psychology and is a member of the Order of Psychologists of Tuscany. She is an active member of the University Laboratory at the University of Florence: Virtual Human Dynamics Laboratory focusing on virtual environments, social psychology, and AI applications.

Giulia Colombini is a psychologist and Ph.D. student in Educational Sciences and Psychology at the University of Florence. She holds a Master’s degree in Clinical Psychology from the University of Padua and from 2020 she is a member of the Virtual Human Dynamics Laboratory at the University of Florence. From 2023 she participates into the international research project Resilience, Personality and Fear of Vaccination coordinated by the University of Leida, on the creation of interculturally sensitive tools to promote vaccination awareness. Her research interests include health behaviors and the study of technological applications for psychological assessment and intervention at clinical and social levels (e.g. social robots, tracking and eye-tracking technologies, approach-avoidance app, virtual reality) and their impact on well-being. She is currently investigating processes of presence perception and immersion in virtual environments.

Andrea Guazzini, Associate Professor and responsible for the laboratory for the study of the human virtual dynamics (VirtHuLab), at the Centre for the Study of Complex Dynamics (CSDC), of the University of Florence. Prof. Andrea Guazzini graduated in Psychology from the University of Florence, and has Ph.D. in Complex system and non-linear dynamics (Centre for the study of complex dynamics - CSDC - University of Florence). From 2012 he is a researcher at the Department of Education and Psychology, University of Florence, and responsible of the VirtHuLab. He was involved in several EU projects both as principal investigator as well as partner, exploiting the VirtHuLab to get experiments and laboratory research. Research interests are in the field of social cognition and virtual social dynamics, experimental and cognitive psychology, neuropsychology. In particular, he worked on the connection between the statistical mechanics and the nonlinear physics to cognitive and social psychology. He is the author of about 120 contributions in international refereed journals (180 publications in total).

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