Developing a Comprehensive Manipulation for the Construct Self-Brand Connection

Abstract

Self-brand connection (SBC) has been manipulated in different ways, often tautologically. This paper provides an effective SBC manipulation that can be used across symbolic and functional brands in online and offline settings, demonstrating internal and external validity. We tested our manipulation in a 2 (SBC: High vs. Low) × 4 (Brands: Audi, Benz, BMW, Porsche) in the USA and in a 2 (SBC: High vs. Low) × 2 (Brands: Samsung, Apple) in the UK on manipulation check and brand attitude (BA) measures, controlling for gender, brand familiarity and brand ownership. Results show that our SBC manipulation has a strong main effect (ω² = .47 experiment 1; .39 experiment 2) and the effect does not vary with the brands. Market research firms can use different elements from our manipulation, such as enhancing congruency of personalities, brand aspiration, desirability, impression management, and word of mouth, to develop advertisements and promotions for the target consumers.

Keywords

brands brand attitude self-brand connection experimental manipulation effect size

Introduction

Over the past two decades, self-brand connection (SBC) has become a versatile construct and an important variable to study in consumer-brand relationships. Researchers have attempted to explore the SBC construct in various ways by treating it as an independent variable (Ferraro et al., 2013), as a moderator (Song et al., 2017), as a dependent variable (Escalas and Bettman, 2003), and also as a mediator (Escalas, 2004).

Despite its importance, SBC has been measured using different scales (Escalas and Bettman, 2003; Jang et al., 2024; Shukla et al., 2024). There has been a debate about manipulating the target variable rather than measuring it to establish a better cause-and-effect relationship (Khan, 2011; Perdue and Summers, 1986; Viglia et al., 2021). A Google Scholar search shows SBC has been experimentally manipulated in only a few studies (Refer to Table 1). Further, in most cases, the authors did not control for the effect of brand ownership (BO), familiarity with the brand (BF), product category (PC), or gender. The lack of control of these variables may influence the manipulation and, therefore, may provide alternate explanations for the intended effect of the manipulation. In this context, we created a comprehensive manipulation for SBC. We used randomised experimental designs to test our manipulation and establish causal inference, while removing alternative explanations (Klarmann & Feurer, 2018). Moreover, more and more published studies exploring SBC are using online platforms to investigate the effects of SBC (Gaustad et al., 2018, 2019). Hence, it is essential to have an effective manipulation that works effectively using online platforms.

Table 1.

Summary of Effect Sizes of Manipulated SBC

Articles	Reported result	Sample size	Omega square (ω²)	Manipulation check	Covariates
Wilson et al. (2017)	F (1,135) = 63.59, p < .01	139	.31	3 items 7-point behavioral intention	NA
Song et al. (2017)	t (241) = 2.44, p < .02	249	NA	7 items 7-point SBC scale	NA
Gaustad et al. (2018)	F (1, 128) = 74.43, p < .01	130	.36	7 items 100-point SBC scale	Initial SBC and BA
Gaustad et al. (2019)	F (1, 524) = 215.20, p < .01	526	.29	7 items 7-point SBC scale	Initial SBC and BA
Wang and John (2019)	F (1, 78) = 12.30, p < .01	80	.14	5 items 11-point brand -self-connection scale (BSC)	NA
Khalifa and Shukla (2021)	F (1, 152) = 81.28, p < .01	154	.35	7 items 7- 7-point SBC scale	NA
Khalifa and Shukla (2021)	F (1, 268) = 294.83, p < .01	270	.52	7 items 7-point SBC scale	NA
Khalifa and Shukla (2021)	F (1, 688) = 473.43, p < .01	690	.41	7 items 7-point SBC scale	NA
Shukla et al. (2024)	F(1, 209) = 853.42, p < 0.01	211	.80	6 items 7-point SBC scale	NA
Xiao et al. (2024)	F (1, 298) = 59.36, p < .01	302	.16	7 items 7-point SBC scale	Image attractiveness, service quality perceptions
Jang et al. (2024)	t(30) = 4.08, p < .01	32	NA	2 items 11-point BSC scale	NA
Jang et al. (2024)	t(207) = 4.59, p < .01	265	NA	2 items 11-point BSC	NA
This research (experiment 1)	F (1,365) = 329, p < .01	376	.47	7 items 7-point SBC scale	Gender, BF, BO
This research (experiment 2)	F (1,297) = 193.98, p < .01	304	.39	7 items 7-point SBC scale	Gender, BF, BO

This paper develops a manipulation for SBC that works for both conditions (high vs low), controlling for the influence of BO, BF, and gender on the SBC manipulation check and brand attitude (BA). To that end, we employed a between-subjects experimental design with random assignment of participants that demonstrates internal validity by including gender, BF, and BO as covariates and shows external validity by using real brands in two different categories. We further limited PC within experiments to address alternate explanations that may arise due to different categories. We thus contribute to experimental research methodology by designing a comprehensive manipulation of SBC using automobile and mobile phone brands which represent both symbolic and utilitarian value, as pointed out by Aaker (1997). Managers can use our manipulation to assess the elements available to enhance consumer brand relationships, making the products more proximal to the potential consumers, and designing advertisements with varying construal level messages based on the SBC (Kim et al., 2021). Managers with an enhanced SBC can expect to have positive word of mouth, brand loyalty, and resistance to switching (Hemsley-Brown, 2023).

Literature Review

Self-Brand Connection

SBC is the “measure of the degree to which consumers have incorporated the brand into their self-concept” (Escalas and Bettman, 2003, p. 340). We know that symbolic brands are important in constructing and extending one’s self-concept (Escalas and Bettman, 2003, 2005; Ferraro et al., 2013) through the self-enhancement motive for aspirational brands (Escalas and Bettman, 2003).

Research shows that gender, ownership, and familiarity (Ferraro et al., 2013), and PC (Swaminathan et al., 2007), a certain level of liking and involvement (Beggan, 1992; Kirmani et al., 1999) and usage (Sprott et al., 2009) of the object, each influence SBC. Sprott et al. (2009) also showed that brand associations can be formed with liked brands even when they are not owned. To minimise the effects of gender, BO, and BF, we controlled for these as covariates. We chose brands that are both aspirational and functional to generalise our results across PCs.

Existing SBC Manipulations

A review of top journals in the field of marketing including the Journal of Marketing Research, Journal of Business Research, Psychology & Marketing, Journal of Academy of Marketing Science, International Journal of Research in Marketing, Journal of Consumer Psychology, Journal of Product and Brand Management, European Journal of Marketing and Journal of Consumer Research shows the papers manipulated SBC (Refer to Table 1 for full details). Researchers have provided comprehensive checklists for creating good experimental manipulations (Chester and Lasko, 2021; Meyvis and Van Osselaer, 2018). Based on the suggestions for designing an effective manipulation, we note that, instead of identifying the desired effect of the manipulation through experiments, it would be prudent to pre-test the manipulation as a standalone manipulation where the variables intended are manipulated and the effect is seen on manipulation check measures, controlling for potential covariates, thereby eliminating any potential confounds.

Prior manipulations have used different approaches to manipulate SBC. However, the approaches open up issues of internal validity, confounding, and asymmetrical manipulations for conditions (high vs low) due to allowing participants to choose their brand, leading to potential idiosyncratic brand effects (Gaustad et al., 2018, 2019; Khalifa and Shukla, 2021; Shukla et al., 2024), using different non-comparable brands in different conditions (Wang & John, 2019; Wilson et al., 2017), not testing the manipulation separately but using it directly in an experiment with other variables (Baghi and Antonetti, 2021; Jang et al., 2024; Shukla et al., 2024; Song et al., 2017; Wilson et al., 2017; Xiao et al., 2024), and using fictitious brands (Baghi and Antonetti, 2021; Jang et al., 2024; Xiao et al., 2024). Further, in most cases, the authors did not control for the effect of BO, BF, PC, or gender, all of which may influence the manipulation effect and provide alternate explanations for the intended effect of the manipulation.

As a starting point, we examined the effect sizes of the SBC manipulations on different manipulation check measure/s (see Table 1). We evaluated the effect sizes of the construct SBC (see Table 1) by manually calculating and converting effect sizes from various forms to Cohen’s d and Omega Squared (Lakens, 2013). We also used online calculators to get the effect sizes (Lenhard & Lenhard, 2022; Uanhoro, 2017).

The variation in effect sizes for the above manipulations (See Table 1) may be due to several reasons, such as not all studies included potential covariates (such as gender, BO and BF), not eliminating brand effects and not restricting PCs, which might confound the findings. This may result in incorrectly capturing variance in the effect size of the manipulation.

Hypothesis Development

Several positives came out of some of the manipulations, such as limiting the PC (Baghi and Antonetti, 2021; Jang et al., 2024; Song et al., 2017; Wang & John, 2019; Wilson et al., 2017; Xiao et al., 2024), using real brands for external validity (Gaustad et al., 2018, 2019; Khalifa and Shukla, 2021; Shukla et al., 2024; Song et al., 2017; Wilson et al., 2017), and assessing initial brand attitude and SBC (Gaustad et al., 2019). Each of the existing manipulations has strengths and weaknesses in its unique application situation. The overarching methodological shortcomings of these manipulations are confounding effects in the absence of controlling for relevant covariates, such as gender, BF, and alternative explanations due to not controlling for BO effects. We eliminated confounding and brand effects by controlling for these covariates.

Researchers have suggested controlling for covariates to demonstrate a successful manipulation (Foster, 2010; Klarmann and Feurer, 2018). For instance, Ferraro et al. (2013) show how gender, ownership, and familiarity each influence SBC. While gender and BF are potential covariates, BO has the potential to confound the effect of SBC manipulation with brand-specific ownership effects, thus being an alternative explanation to the manipulation effects. Finally, many of these manipulations did not test the manipulation as a standalone manipulation; instead, they were used with other variables in an experiment (Jang et al., 2024; Shukla et al., 2024; Song et al., 2017; Xiao et al., 2024), meaning the effects detected could have come from other sources. We account for these issues in our manipulation. Hence, our goal is to chart a path to a standardised manipulation that will work irrespective of the context, brand, gender and BO in symbolic and utilitarian products such as automobiles and mobile phones (Aaker, 1997). Therefore, we propose.

There will be a significant main effect of the SBC manipulation on the SBC manipulation check.

There will be no significant main effect of the brands on the SBC manipulation check.

There will be no significant interaction effect of brands and SBC manipulation on the SBC manipulation check.

Gender, BO, and BF will significantly affect the SBC manipulation check.

There will be a significant main effect of the SBC manipulation on the BA.

There will be no significant main effect of the brands on the BA.

There will be no significant interaction effect of brands and the SBC manipulation on the BA.

Gender, BO and BF will significantly affect the BA.

The Present Manipulation

We consciously tapped into the latent variables of the construct SBC, such as brand personality, emotional affect, impression management, and word of mouth, to develop the manipulation. We did not use the SBC definition and measurement scale to develop manipulation, as has been done in a few studies (Shukla et al., 2024; Song et al., 2017). We developed an SBC manipulation that embodies the process of generating brand associations by identifying brand personalities that participants share (or do not share) with a target brand for each SBC condition (high vs low). Research shows that brand personality matters in forming consumer brand relationships (Malär et al., 2011; McManus et al., 2021; Swaminathan et al., 2009). Additionally, based on SBC conditions (high vs low), we made the participants provide a review (positive vs less positive) of the brand to a person who is unsure of buying the brand. Shen and Sengupta (2018) shows that speaking positively about a brand results in a strong connection with the brand. Additionally, we used real brands for our manipulation to provide external validity, measuring the effect of the manipulation on the manipulation check and also on BA for nomological validity. We chose automobile brands because consumers tend to have a more intense relationship with automobile brands than with other PCs (Fetscherin et al., 2014) and can act as a status symbol (Hamzaoui & Merunka, 2006) with aspirational value, and automobiles also possess utilitarian value (Aaker, 1997). We also replicated our manipulation using mobile phone brands which cater to symbolic and functional value (Aaker, 1997) to generalise the manipulation across PCs.

Considering that several SBC studies are using online systems to recruit participants (Gaustad et al., 2019; Shukla et al., 2024; Xiao et al., 2024), we took care to make our manipulation online user-friendly, with an estimated time of around 3 min only for the manipulation, to minimise attrition rates and improve online response rates (Aguinis et al., 2021).

Experimental Method and Results

Experiment 1

The study was approved by the Faculty Human Ethics Advisory Group, (HEAG), Deakin University (BL-EC-4-20) on March 03, 2020.

A 2 (SBC: High vs. Low) × 4 (Brands: Audi vs. Benz vs. BMW vs. Porsche) between-subject experimental design was employed on MTurk using Qualtrics to investigate the effect of our SBC manipulation on the manipulation check measure (Escalas and Bettman, 2003). We controlled for the influence of gender, BO, and BF as covariates. The sample size was calculated using .31 as the lowest effect size in the literature (see Table 1) to achieve an adequate power of 0.8.

Participants were recruited via Amazon’s MTurk, and 376 participants responded successfully (173 Male (46%) and 203 Female (54%)); M_age = 41 years (SD_age = 13.46).

Procedure

Participants were randomly assigned to one of 8 conditions. The appendix provides the detailed manipulation. Table 2 below provides the measures completed by the participants after being exposed to the manipulation, the reliability, and items for each measure. Measurement scales can be found in the appendix.

Table 2.

Summary of Reliability of the Measures

Measures	Cronbach’s alpha	Number of items
Actual self-congruity high SBC (Malär et al., 2011)	.92	2
Actual self-congruity low SBC (Malär et al., 2011)	.95	2
SBC (Escalas and Bettman, 2003)	.98	7
Brand distinctiveness (Stokburger-Sauer et al., 2012)	.91	3
Brand prestige (Stokburger-Sauer et al., 2012)	.94	3
Brand symbolism (Escalas and Bettman, 2005)	.91	2
Brand signalling (Park and John, 2010)	.97	4
Brand attitude (Cheah et al., 2015; MacKenzie and Lutz, 1989)	.99	4
Brand aspiration (Escalas and Bettman, 2003)	.83	2
Brand familiarity (Zhou et al., 2010)	.76	3
Impression management (Escalas and Bettman, 2003)	.55	2

Results and Discussion

Table 3 in the appendix shows the means (SDs) of participant ratings of all parameters for each of the brands for SBC conditions (high vs low). The survey concluded with collecting information on participants’ demographics, after which they were paid for their time.

Results

The effect of the manipulation on SBC was analysed as a 2 (SBC: High vs. Low) × 4 (Brands: Audi vs Benz vs. BMW vs. Porsche) factorial ANOVA. We bootstrapped the sample 5000 times. As anticipated, this revealed a significant main effect of the SBC manipulation (F (1,365) = 329.19, p < .01, η²_p = .47; ω² = .47), with participants in the high SBC condition (M_HSBC = 4.18; SD_HSBC = 1.71) reporting significantly higher SBC than participants in the low SBC condition (M_LSBC = 1.58; SD_LSBC = 1.04). Thus, H1 is supported, showing the success of SBC manipulation.

The main effect of brands was not statistically significant (F (3,365) = 1.16, p = .32; η²_p = .01; ω² = .00). Thus, H2 is supported, indicating that the brands did not influence the SBC check measure.

The interaction effect between brands and the SBC manipulation was also not significant (F (3,365) = 1.74, p = .16; η²_p = .01; ω² = .01). This study thus supported H3, showing that the effectiveness of the SBC manipulation did not vary across brands (see Table 5a in the appendix and Figure 1). We demonstrate that our SBC manipulation is successful with only a significant main effect of SBC and the absence of any brand effect or interaction effect between brands and SBC manipulation.

Figure 1.

Interaction effect of the SBC manipulation and brands on the SBC measure while controlling for BF, BO, and gender

As covariates, we included gender, BF, and BO as they tend to influence SBC (Ferraro et al., 2013). BF was significant (F (1,365) = 27.17; p < .01; η²_p = .07; ω² = .10). BO was significant (F (1,365) = 6.12; p < .02; η²_p = .02; ω² = .01). Gender was not significant (F (1,365) = .48; p = .49; η²_p = .00; ω2 = .01). Thus, H4 is partially supported. Table 5a in the appendix provides the estimated means (standard error) of the manipulation on the SBC manipulation check measure for all brands across SBC conditions (high vs. low).

We show the effect of the SBC manipulation on BA for nomological and predictive validity. As expected, the main effect of SBC manipulation on BA was significant (F (1,365) = 425.71, p < .01; η²_p = .55; ω² = .54). Thus, the study supported H5. The main effect of brands on BA was not significant (F (3,365) = .73, p = .54; η²_p = .01; ω² = .00), nor was the interaction effect of SBC manipulation and brands on BA (F (3, 365) = .77, p = .51; η²_p = .01; ω² = .00). Thus, the study supported H6 and H7. The manipulation works as intended, with only SBC manipulation determining BA and no brand main effect or interaction effect (see Figure 2).

Figure 2.

Interaction effect of SBC manipulation and brands on BA while controlling for BF, BO, and gender

In predicting BA, we found BF was significant (F (1,365) = 18.16; p < .01; η²_p = .05; ω² = .05), as was BO (F (1,365) = 6.95; p < .01; η²_p = .02; ω² = .02), whereas gender was not significant (F (1,365) = .00; p = .95; η²_p = .00; ω² = .00). Thus, H8 is partially supported. See Table 5b in the appendix for the overall average estimated means of BA for all brands across (high vs. low) SBC conditions when accounting for these covariates.

Experiment 2

The study (2025/HE001109) was approved by the Faculty Human Ethics Advisory Group, (HEAG), Deakin University on September 09, 2025.

We replicated experiment 1 with mobile phone brands (Apple vs. Samsung) to extend the SBC manipulation to different PC brands. We collected the data via Prolific as it provides higher-quality data and the samples are more diverse compared to Amazon’s Mechanical Turk or CrowdFlower (Peer et al., 2017).

304 participants responded successfully (140 Male (46%) and 162 Female (53.3%); Mage = 45.4 years SDage = 13.41).

Procedure

We followed the same procedure as provided in experiment 1 in terms of measures and manipulation instructions.

Results and Discussion

A summary table is provided in the appendix (see Table 4) showing the means (SDs) of participant ratings of all parameters measured for each of the brands for SBC conditions (high vs. low).

Results

The effect of manipulating SBC on the manipulation check was analysed as a 2 (SBC: High vs Low) × 2 (Brands: Apple vs Samsung) factorial ANOVA. We bootstrapped the sample 5000 times. We found a significant main effect of the SBC manipulation (F (1,297) = 193.98, p < .01, η²_p = .40; ω² = .39), with participants in the high SBC condition (M_HSBC = 4.47; SD_HSBC = 1.56) reporting significantly higher means than participants in the low SBC condition (M_LSBC = 2.17; SD_LSBC = 1.19). H1 is supported, showing the success of SBC manipulation.

The main effect of brands on the manipulation check was statistically significant (F (1,297) = 6.92, p < .01; η²_p = .02; ω² = .02). H2 is not supported. This is an interesting finding, as it differs from Experiment 1. This is discussed later. The interaction effect between brands and the SBC manipulation was not significant (F (1,297) = 2.42, p = .12; η²_p = .01; ω² = .01). Thus, H3 is supported, showing that the SBC manipulation did not vary across brands as desired (see Table 6a in the appendix and Figure 3). We demonstrate that our SBC manipulation is successful with a significant main effect of the SBC manipulation and no interaction effect between brands and SBC manipulation.

As covariates, we included gender, BF, and BO as they tend to influence SBC (Ferraro et al., 2013). BF was significant (F (1,297) = 8.86; p < .01; η²_p = .03; ω² = .03). BO was significant (F (1,297) = 19.40; p < .01; η²_p = .06; ω² = .06). Gender was not significant (F (1,297) = 1.26; p = .26; η²_p = .00; ω² = .00). Thus, H4 is partially supported.

Table 6a in the appendix provides the estimated means (standard error) of the SBC manipulation on the SBC manipulation check measure for all brands across SBC conditions (high vs. low)

Figure 3.

Interaction effect of the SBC manipulation and brands on the SBC measure while controlling for BF, BO, and gender

We also show the effect of the SBC manipulation on BA for nomological and predictive validity. As expected, the main effect of SBC manipulation on BA was significant (F (1, 297) = 171.47, p < .01; η²_p = .37; ω² = .36). H5 is supported. The main effect of brands on BA was not significant (F (1.297) = 3.33, p = .07; η²_p = .01; ω² = .01), nor was the interaction effect of SBC manipulation and brands on BA (F (1,297) = 2.19, p = .14; η²_p = .01; ω² = .00). Thus, the study supported H6 and H7. The manipulation works as intended, with only SBC manipulation determining BA with no brand main effect or interaction effect (see Figure 4).

In predicting BA, we found BF was significant (F (1,297) = 3.93; p < .05; η²_p = .05; ω² = .01), as was BO (F (1,297) = 21.70; p < .01; η²_p = .01; ω² = .01), whereas gender was not significant (F (1,297) = 1.18; p = .28; η²_p = .00; ω² = .00). Thus, H8 is partially supported. See Table 6b in the appendix for the overall average estimated means (standard error) of the SBC manipulation on BA for all brands across SBC conditions (high vs. low)

Figure 4.

Results of SBC and brands on BA while controlling for BF, BO, and gender

General Discussion

The current paper provides a novel manipulation of SBC (high vs low), controlling for the effects of gender, BO, and BF as covariates on the SBC manipulation check and on BA. We generalised the findings by performing experiment 1 with automobile brands and experiment 2 with mobile brands. Our manipulation has a very large effect size (ω² = .47 and .39), the third strongest of all those reported effect sizes for SBC manipulations in the literature (see Table 1). The studies which had a larger effect size than our research did not control for covariates like gender, BF, and BO (Khalifa and Shukla, 2021; Shukla et al., 2024).

We found no interaction effect between brands and SBC manipulation (high vs low) for both experiment 1 and experiment 2 on the manipulation check and BA. This is a critical finding demonstrating that the manipulation is not influenced by brand effects. We find no brand effect in experiment 1 on the SBC check, but we detect a brand effect in experiment 2. This is an interesting finding and ties to BO and BF. Brands which are more familiar and more owned seem to provide a brand effect on the SBC manipulation check. The detection of the main effect of brands in experiment 2 indicates that brands with high ownership do affect SBC formation. Data shows 65% of the sample owned the brands for experiment 2, whereas only 6% of the sample in experiment 1 owned the brands. Our manipulation took care of the concern by controlling for ownership. This suggests that our SBC manipulation is successful with the target brands while controlling for the effect of gender, BF, and BO. Thus, the results show that this manipulation is successful in manipulating SBC while avoiding potential confounding effects due to BO, and alternate explanations that may arise due to BF, and gender. We found the effect of BO is comparatively stronger than BF in both experiment 1 and 2 on the SBC manipulation check and BA. However, both the effects of BO and BF are very small compared to the SBC manipulation effect on the SBC check and BA. This demonstrates that SBC can move the BA in both SBC conditions, despite controlling for all the covariates. This also takes care of any existing initial SBC and BA towards a target brand.

Managerial Contributions

This paper contributes methodologically to the consumer brand literature by developing a manipulation for SBC, tapping into the latent variables rather than creating a manipulation based on definition and measurement scales, avoiding any tautological issues with the manipulation effect. Brand managers can test our manipulation in enhancing SBC by measuring their initial SBC and post-manipulation SBC. Furthermore, market research firms can use different elements from our manipulation, such as enhancing congruency of personalities, brand aspiration, desirability, impression management, and word of mouth, to develop advertisements and promotions for the target consumers. Furthermore, brand managers can use our 3-min manipulation in-store or online, while enrolling consumers in their loyalty programs or running promotions.

Limitations and Future Directions

While an important methodological advancement in the field of SBC, there are nonetheless future research avenues. Future research should consider mapping the effect size of SBC using symbolic and functional PCs in a factorial experiment, such as clothing, watches, and personal accessories, electronics etc. This will enable future researchers to determine whether there is more latent resistance to SBC manipulation among some categories as compared to others. Furthermore, the manipulation can be further abridged to find an optimum effort to effect ratio. Market research firms can use our manipulation to get practical insights as to which elements of the manipulation are more effective than others in building SBC.

Conclusion

This paper provides a generalised experimental manipulation for the construct SBC while controlling for the potential effects of gender, BF, and BO. SBC has largely been measured for a target brand with which the participants have either familiarity or have owned it. Thus, our research contributes to the domain of consumer-brand relationships by providing a generalised experimental manipulation for SBC controlling for gender, BF, and BO. This manipulation for both conditions of SBC (high vs low) can be used across symbolic and functional brands in future studies.

Supplemental Material

Supplemental Material - Developing a Comprehensive Manipulation for the Construct Self-Brand Connection

Supplemental Material for Developing a Comprehensive Manipulation for the Construct Self-Brand Connection by Partha Sarathi Datta, Chris Dubelaar in International Journal of Market Research

Footnotes

ORCID iD

Partha Sarathi Datta

Ethical Considerations

This project is part of Dr Partha Sarathi Datta’s thesis work. The study was approved by the Faculty Human Ethics Advisory Group, (HEAG), Deakin University (BL-EC-4-20) on March 03, 2020, and again (2025/HE001109) on September 09, 2025.

Consent to Participate

All participants provided written informed consent before participating. Informed consent to participate in the study was in written form before the study. Participants had a choice to participate or not to participate. Participants were fairly paid for their participation.

Author Contributions

Conceptualization: [Dr Partha Sarathi Datta], Methodology: [Dr Partha Sarathi Datta], Formal analysis and investigation: [Dr Partha Sarathi, Dr Chris Dubelaar], Writing - original draft preparation: [Dr Partha Sarathi Datta], Writing - review and editing: [Dr. Partha Sarathi Datta, Dr Chris Dubelaar], Funding acquisition: [Deakin University], Resources: [Deakin University], Supervision: [Dr Chris Dubelaar].

Funding

The authors did not receive support from any organization for the submitted work. This project is part of Dr. Partha Sarathi Datta’s thesis work. The work is financially supported by Dr Partha Sarathi Datta’s PhD grant from Deakin University.

Declaration of Conflicting Interests

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

Data Availability Statement

The data is stored in the Open Science Framework depository. The data is available in the link provided.

Supplemental Material

Supplemental material for this article is available online.

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