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Predicting fruit consumption: A multi-group application of the Theory of Planned Behavior
Journal Pre-proof Predicting fruit consumption: A multi-group application of the Theory of Planned Behavior Luigina Canova, Andrea Bobbio, Anna Maria Manganelli PII:
S0195-6663(19)30754-8
DOI:
https://doi.org/10.1016/j.appet.2019.104490
Reference:
APPET 104490
To appear in:
Appetite
Received Date: 10 June 2019 Revised Date:
19 September 2019
Accepted Date: 12 October 2019
Please cite this article as: Canova L., Bobbio A. & Manganelli A.M., Predicting fruit consumption: A multi-group application of the Theory of Planned Behavior, Appetite (2019), doi: https://doi.org/10.1016/ j.appet.2019.104490. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Predicting Fruit Consumption: A multi-group application of the Theory of Planned Behavior
Authors: Luigina Canova, PhD, Andrea Bobbio, PhD, Anna Maria Manganelli Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Italy
Corresponding Author Luigina Canova, Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Via Venezia 14, 35131, Italy. Phone : +390498276686, Fax: +39 0498274719, E-mail: [email protected]
Conflict of Interest Disclousure: The authors have no conflict of interest.
The research project was funded by the University of Padua: ‘Ex 60%’ Scientific Research Funds – 60A17-3217/12.
Predicting Fruit Consumption: A multi-group application of the Theory of Planned Behavior
ABSTRACT The main aim of the study was to test a two-wave longitudinal prediction model for the consumption of at least three portions of fruit per day, that was grounded on the Theory of Planned Behaviour (TPB; Ajzen, 1991), extended with measures of past behaviour and self-identity as a healthy eater. Self-identity is defined as a salient part of the self-concept specifically relates to a desirable behaviour, such as, in this case, healthy eating. A second aim of the study was to test the invariance of the proposed model in two samples of Italian university students (n=208) and no-student adults (n=321). At time 1 the questionnaire was made up of measures of TPB constructs, with the addition of past consumption and self-identity as a healthy eater. Both the affective and evaluative components of attitude were assessed. At time 2, only the target behaviour was surveyed. After checking both adequacy of the measurement model and reliability estimates, data were analysed via structural equation modelling that returned good fit indices. Results showed that intention was positively associated with subjective norm, perceived behavioural control, self-identity and past behaviour. Altogether, they explained 78% of the intention variance in the student group, and 81% in the adult group. After controlling for intention, past behaviour – but not self-identity – was significantly related to the selfreported behaviour, and the model explained 69% and 62% of behaviour variance, respectively. Multisample analysis supported model invariance across the two groups. Results and their possible applications are presented and discussed.
Predicting Fruit Consumption: A multi-group application of the Theory of Planned Behavior
1. Introduction Fruit and vegetables are important components of a balanced diet. A joint report of the World Health Organization and the Food and Agriculture Organization (WHO, 2003) recommends the consumption of at least 400 grams of fruit and vegetables per day as a minimal quantity to prevent chronic illnesses and obesity. This amount corresponds to three portions of fruit and two portions of vegetables. Despite the well-known evidence that fruit and vegetables are key components of a healthy diet, in Italy, between 2014 and 2017, only 10% of those interviewed aged 18–69 years ate five portions of fruit and vegetables per day (Istituto Superiore di Sanità, 2019). Consumption increases with age, is more frequent among women, and among wealthier and more educated people. The present study focused on the consumption of three portions of fresh fruit per day and aimed to identify the determinants of this behavior in young and adult people. The Theory of Planned Behavior (TPB; Ajzen, 1991) was assumed as theoretical reference model. According to TPB, the immediate antecedent of a behavior is intention to perform it. Intention is influenced by attitude toward the behavior, subjective norm, and perceived behavioral control (PBC). PBC contributes to the prediction of both intention and behavior. The TPB has been already successfully applied to a wide range of behaviors including healthy eating (e.g., Conner, Norman, & Bell, 2002; McEachan, Conner, Taylor, & Lawton, 2011), eating dietary behaviors (e.g,. Armitage & Conner, 1999; Brouwer & Mosack, 2015), fruit and vegetables consumption (e.g., Blanchard et al., 2009a; Blanchard et al., 2009b; Godin, Amireault, Bélanger-Gravel, Vohl, Pérusse, Guillaumie, 2010). The present research, conducted in the Italian context, analyzes the efficacy of TPB in the prediction of both intentions and action for a specific behavior, which is consuming three servings of fresh fruit per day.
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The original TPB model has been extended by decomposing some constructs in two or more facets or by exploring a number of additional predictors. In the model hypothesized in this study, we distinguished two attitude components (affective and cognitive) and considered two additional predictors: past behavior and self-identity. As regards attitude, the affective component reflects the emotion-based judgments about the behavior; the cognitive component represents its anticipated consequences, benefits, or costs. Previous studies showed that affective attitude was the dominant attitudinal predictor of intention in the case of healthy behaviors (McEachan et al., 2016) and of the consumption of five servings of fruit and vegetables a day (Blanchard et al., 2009a; Blanchard et al., 2009b). Regarding additional predictors, a number of studies considered past behavior (e.g., Carfora, Caso, & Conner, 2016; Hagger, Polet, & Lintunen, 2018) and self-identity (e.g., Carfora et al., 2016; Rise, Sheeran, & Hukkelberg, 2010; Reid, Sparks, & Jessop, 2018). The inclusion of past behavior within TPB is especially justified when the behavior is seen to be influenced by habit (Conner & Armitage, 1998). In fact, the repeated performance of a behavior, such as in the case of fruit consumption, transfers it from a reasoned process, as described by TPB, to an automatic process (Conner & McMillan, 1999). The contribution of past behavior to the prediction of intention and behavior when TPB constructs are controlled is of particular interest. For example, McEachan et al.’s (2011) meta-analysis showed that past behavior increased the quotas of explained variance in healthy intentions and behaviors. However, a recent meta-analysis by Hagger et al. (2018) showed that the inclusion of past-behavior resulted in a significant attenuation of the direct effect of intention on behavior and of the indirect effects of the other TPB constructs on behavior. Self-identity, or “the salient part of the actor’s self which relates to a particular behavior” (Conner & Armitage, 1998, p. 1444), can be considered as a set of enduring characteristics that people ascribe to themselves (e.g. “I think of myself as a green consumer”; Sparks, 2000). According to identity theories (Stryker, 1987), people use several context-dependent socially meaningful 2
categories in describing themselves in terms of, for example, sociodemographic characteristics (e.g. gender), social roles (e.g. mother, father), social types (e.g. smoker, healthy eater, blood donor, green consumer). Specific expectations associated with these categories guide people's motivation and behavior, so that people usually plan and act in accordance with their self-identity. Fishbein and Ajzen (2010) argued that self-identity overlaps conceptually with past behavior, as people could infer selfidentities through the examination of past behaviors. If this is the case, self-identity should not have an independent effect on intention when past behavior is accounted for. Moreover, according to attitude theorists (Eagly & Chaiken, 1993), self-identity also shows a conceptual overlap with attitude. Despite ongoing debates, the findings of a meta-analytic study by Rise et al. (2010) showed that self-identity explained additional variance in intentions and that its effects were independent from both past behavior and other TPB constructs. Other studies in a number of behavioral contexts (Dean, Raats, & Shepherd, 2012; Dunn, Mohr, Wilson, & Witterta, 2011; Jung & Bice, 2019; Ries, Hein, Pihu, & Armenta, 2012; Reid et al., 2018) supported the conclusions of this meta-analysis. In addition, a subgroup of studies considered in the meta-analysis of Rise et al. (2010) included a prospective measure of behavior; the inclusion of self-identity engendered a significant increase in the explained behavior variance; and self-identity had a direct influence on behavior even after intention and PBC had been taken into account. However only a few studies examined the effect of self-identity on behavior when past behavior is controlled (for a review see Carfora et al., 2016). In the field of food choices, self-identity as a healthy eater is often considered. When individuals see themselves as a healthy eater, they should regulate their behavior consistently with both expectations and activities associated with being a healthy eater. Brouwer and Mosack (2015) found that the healthy eater identity predicted the intention to eat a healthy diet (i.e., low-fat dairy, fruit, vegetables, and whole grains) and that it was a significant predictor of low-fat dairy and fruit but not of vegetables and whole grains consumption. In the Italian context, two studies (Carfora et al., 3
2016; Caso, Carfora, & Conner, 2016) on adolescents’ and university students’ eating five portions of fruit and vegetables a day showed that self-identity as a healthy eater had direct effects both on intention and behavior after controlling for past behavior.
1.1. Aims of the study In sum, the first aim of the present study was to test the validity of a TPB model extended with past behavior and self-identity as a healthy eater applied to the consumption of three portions of fresh fruit per day. As mentioned before, we distinguished two attitude components: cognitive and affective. Based on previous research, we expected only the affective component of attitude to be associated with intention and, indirectly, with behavior. We predicted also that, after controlling for TPB constructs, past behavior and self-identity would account for additional variance of intention and behavior and affect them both. Direct effects of past behavior on future behavior may reflect non-conscious processes, while indirect effects through social cognitive variables may represent reasoned processes. When the target behavior is expected to be performed frequently, such as in the case of daily fruit consumption, thus offering greater opportunity for habit formation, it is probable that past behavior would exert larger direct effects than intentions on future behavior (Hagger et al., 2018). In addition, self-identity may influence directly the behavior because it produces a standard of reference for it (Stryker & Burke, 2000), allowing individuals to assess the perceived overlapping between their identities and the meanings of behavior. The second aim concerned the generality of the processes leading to intention formation and, ultimately, to behavior in different categories of individuals, that is, Italian university students and non-student adults. Research considering the type of sample as a moderator of the relationships within TPB are rare. In two meta-analyses by McEachan et al. (2016) and McEachan et al. (2011) the comparisons between three different age groups (adults, university students, and adolescents) showed that, in the case of dietary behaviors, the differences in the strength of the relationships 4
between attitude, norms, PBC, and intention emerged only in adolescents/school-aged versus adult samples. However, in these meta-analyses the range of behaviors taken into account was very broad, going from “healthy eating” – a general target – to the consumption of “five portions of fruit and vegetables” – which is, on the contrary, much narrowed. Instead, to our knowledge the number of studies that aimed to test the invariance of the processes leading to the formation of intention and behavior in the case of specific behaviors, considering different groups within the same population or different cultural contexts, are very scarce (an exception is represented by Ries et al., 2012, which tested the invariance of a TPB extended model among Spanish and Estonian students samples). Supporting the invariance of the over mentioned processes might be of great interest in order to design intervention programs which, at the same time, may target different subgroups of a population. Based on the results of the meta-analyses mentioned above, we expected to confirm invariance of the processes leading to the formation of intention and then to behavior in both university students and adults.
2. Method 2.1. Procedure and Participants The participants completed a structured questionnaire at time 1 (T1), including the measures of the extended TPB model and socio-demographic variables. Two weeks later (T2), the participants’ selfreported behavior measures were collected. The relatively short follow-up was chosen mainly because short time lag (i.e., one or two weeks) is often used when the target behavior is performed frequently, or perhaps daily, as in the case of eating behaviors or making regular exercise (e.g. Blanchard et al., 2009a; Blanchard et al., 2009b; Brower & Mosack, 2015; Caso et al., 2016; Carfora et al., 2016; Conner & Armitage, 1998; Rhodes, Blanchard & Matheson, 2006). Moreover, the short short time lag was preferred in order to reflect optimal predictive accuracy of the model – particularly for the intention-behavior relationship, which could be attenuated by a longer follow-up 5
(McEachan et al., 2011) allowing other factors to change intentions – and to better respect the TPB tenets of time, context, target, and action (Ajzen, 1991). On the contrary, self-identity, which is defined as an individual enduring disposition, is less likely to be subject to significant changes within two weeks. For the adult sample, 100 university students from two courses in Social Psychology at Padua University were engaged in data collection as part of a research–experience assignment. The psychology students were asked to administer the questionnaire to five of their acquaintances or neighbors. All students were trained to deliver an informed consent form that participants had to sign and return before completing the questionnaire at time 1. Informed consent forms and completed questionnaires were collected in separate envelopes, and then returned to the researchers by the students. The participants were informed about the aim of the study, the duration of the task, and the possibility of withholding their consent to participate, and they were assured that all responses would remain confidential. They filled in the questionnaire autonomously and gave it back immediately. Two weeks later, they received the second questionnaire and were quickly debriefed. Among the 500 potential participants, usable data came from 417 individuals (response rate: 83%). Among these, 376 also completed the second questionnaire (final response rate: 75%). In order to obtain a homogeneous sample with regard to eating style, only the participants who declared to be omnivorous were retained. The final adult sample (n = 321) was comprised of 167 women (52%) and 151 men (47%) (3 missing data), aged 37–79 years (M = 53, SD = 7), and mostly lived in Northeast Italy (68%). Two hundred and forty-seven were in the workforce (77%) and 73 (23%) were out of work (housewife, retired, unemployed). In terms of education, 33% completed compulsory schools, 44% reached the high school level, and 21% had a university degree (7 missing data). The mean body mass index (BMI) was 25.6 (SD = 3.3) for men and 23.4 (SD = 3.5) for women.
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As regards the student sample, all of the participants were from Padua University and did not receive course credits or other form of reward for their participation. None of these participants was involved in recruiting the adult sample. The students filled in the questionnaires during regular classes. Among the 308 students responding at T1, a total of 255 (response rate: 82.8%) returned the second questionnaire two weeks later. Our final sample (n=208) was made up of 164 women (79%) and 42 men (20%) (2 missing data) who followed an omnivorous diet. The majority (n = 114, 54.8%) attended the degree course in Sociology, 83 (39.9%) in Education, and 8 (3.8%) attended other courses (3 missing data). The mean age was 19.8 years (SD = 1.2; range: 18–28), and the mean BMI was 22.5 (SD =3.3) for men and 21 (SD = 3) for women. Almost all lived in Northeast Italy (96.2%). The research project obtained approval from the Ethical Committee of the School of Psychology at Padua University (code 2173).
2.2. Measures The questionnaires for adults and students presented the same measures of TPB constructs already used in previous studies in the Italian context (Canova & Manganelli, 2016), that followed the TPB questionnaire construction guidelines (Fishbein & Ajzen, 2010). The target behavior was the consumption of at least three portions of fresh fruit per day in the next two weeks. For all participants the following description was provided: “a portion corresponds to 150 grams of fresh fruit: a medium-sized fruit such as an apple, pear, etc.; two small fruits such as plums, apricots, etc.; and a glass of fresh fruit juice.” In the first wave of the study (T1), participants were asked to report their attitudes, subjective norm, perceived behavioral control, past behavior, self-identity, and demographics information. Attitude towards the Behavior. Attitude was measured by presenting the participants the statement: “to consume at least three portions of fresh fruit per day in the next two weeks would be....”, and 7
asking them to respond on eleven 7-point semantic differential adjective scales that tapped into both cognitive (harmful–beneficial, negative–positive, stupid–intelligent, unhealthy–healthy) and affective (sad–joyful, bad–good, undesirable–desirable, depreciable–appreciable, disagreeable– agreeable, distasteful–tasteful, unpleasant–pleasant) components. The response scales were anchored from 1 (negative pole) to 7 (positive pole). Subjective Norm. The participants were asked to respond on a 7-point Likert scale to four items such as “Most people who are important to me would like me to consume at least three portions of fresh fruit per day in the next two weeks.” The anchors varied for each question, but all lower points (i.e., 1) reflected low agreement, approval, or support, and all higher points (i.e., 7) reflected high agreement, approval, and support. Perceived Behavioral Control. This was measured with four items such as “To what extent do you think consuming at least three portions of fresh fruit per day over the next two weeks is a behavior under your control?” The 7-point response scale presented different anchors for each item with lower points (i.e., 1), which indicate low agreement, easiness, and ability of control, and higher points (i.e., 7), which indicate high agreement, easiness, and ability. Intention. Four items were used such as: “I intend to consume at least three portions of fresh fruit per day over the next two weeks.” Lower points on the response scale (i.e., 1) indicated low agreement, strength of intention, and likelihood, whereas higher points (i.e., 7) indicated higher agreement, strength of intention, and likelihood. Past behavior. The items were as follows: “In the last two weeks, have you consumed at least three servings of fresh fruit a day?”; “In the last two weeks, how often did you consume at least three portions of fresh fruit a day?”; and “In the last two weeks, how many servings of fresh fruit have you consumed per day?” The response scale ranged from 1 (never/no one) to 5 (regularly/everyday/four or more). Self-identity as a healthy eater. To measure self-identity as a healthy eater, eight items derived from the literature were adopted (Carfora et al., 2016; Sparks & Shepherd, 1992; Spark, Shepard, 8
Wieringa & Zimmermanns, 1995; Terry, Hogg, & White, 1999). Example statements included: “I think of myself as a healthy eater”, “I think of myself as a person who is interested in eating healthy”, “I think of myself as a someone who is concerned about the health consequences of what I eat”, “I see myself as someone who eats healthy”, “I am the kind of person who eats healthy”, “Eating healthy is an important part of who I am.” Four of the items used were very similar to the seven prototypical items recently proposed by Snippe, Peters and Kok (2019) as a way to measure self-identity in a wide range of behaviors. The response scale ranged from 1 (strongly disagree) to 7 (strongly agree). Demographics. Gender, age, geographic area of residence, education (or, in the case of student, the course of study), employment status, weight, and height were assessed. Moreover, the participants were asked, “How would you define your diet?” (Omnivorous; Vegetarian; Vegan; Omnivorous, but I follow limitations for health reasons; I follow a kosher/halal regime or other limitations for religious reasons). At T2, the participants had to report their consumption behavior with reference to the two weeks immediately before the second wave of the research. For this purpose, the first two items that captured past behavior at T1 were used again. In particular: “In the last two weeks, have you consumed at least three servings of fresh fruit a day?” with a response scale ranging from 1 (No, never) to 5 (Yes, regularly), and “In the last two weeks, how often did you consume at least three portions of fresh fruit a day?” with a response scale from 1 (never) to 5 (everyday). All measures reliability statistics for the two samples are reported in Table 2.
2.3. Data analysis In order to check whether all measures detected distinct constructs, the goodness-of-fit of the measurement models was assessed via Confirmatory Factor Analysis. Analyses were performed 9
using the maximum likelihood method applied to covariance matrices (LISREL 8.80). Two parcels were created when the number of indicators was greater than two (Bagozzi & Edwards, 1998). Structural equation modeling were estimated to test the hypothesized models of relations. Goodness-of-fit was evaluated by means of χ2, the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). A satisfactory model fit is indicated by χ2 not significant even if it strongly depends on sample size; by CFI values greater than or equal to 0.95, by RMSEA values equal to or lower than 0.06, and by SRMR values equal to or lower than 0.08 (Hu & Bentler, 1999). Cronbach's alpha coefficients, descriptive statistics, and the differences between the mean scores of adults and students were computed for all the variables (t test and Cohen's d). Finally, the multi-sample procedure was used to test whether some or all of the parameters of the model were invariant across groups (Byrne, 1998).
3. Results A measurement model with 8 correlated latent factors and 16 indicators was tested in both samples separately. The goodness-of-fit indices were satisfactory: adult sample, χ2(76) = 137.55, p = .0001, RMSEA = 0.05, CFI = 0.99, SRMR = 0.04; student sample, χ2(76) = 120.46, p = .0008, RMSEA = 0.05, CFI = 0.99, SRMR = 0.04. The standardized factor loadings, which were all significant, showed that each latent factor was defined by its own indicators. The correlations among the latent factors (Table 1) were all significant, and the 95% confidence intervals, which were obtained by considering two standard errors above and below the coefficients, did not include the perfect correlation (i.e., 1.00), thus supporting the fact that all measures captured the distinct constructs in both samples (Bagozzi, 1994).
Table 1. Correlations between TPB latent factors 10
Constructs
Affective Cognitive Attitude Attitude
Subjective Norm
PBC
Intention
Past Behaviour
SelfIdentity
Behaviour
Affective Attitude Cognitive Attitude Subjective Norm
-
0.80(0.05)
0.33(0.07)
0.57(0.06) 0.65(0.05)
0.55(0.06)
0.32(0.07) 0.49(0.06)
0.89(0.04)
-
0.41(0.08)
0.36(0.08) 0.48(0.07)
0.27(0.08)
0.19(0.08) 0.28(0.08)
0.45(0.06) 0.48(0.06)
-
0.23(0.08) 0.53(0.06)
0.35(0.08)
0.21(0.08) 0.40(0.07)
PBC
0.48(0.05) 0.37(0.06)
0.29(0.06)
-
0.74(0.04)
0.72(0.05)
0.53(0.06) 0.58(0.05)
Intention
0.62(0.04) 0.48(0.06)
0.45(0.05)
0.82(0.02)
-
0.77(0.04)
0.57(0.06) 0.75(0.04)
Past Behaviour 0.54(0.05) 0.34(0.07)
0.26(0.06)
0.76(0.03) 0.79(0.03)
-
0.56(0.06) 0.80(0.04)
Self-Identity
0.45(0.06) 0.28(0.07)
0.21(0.06)
0.47(0.05) 0.52(0.05)
0.38(0.06)
-
0.50(0.06)
Behaviour
0.44(0.05) 0.25(0.06)
0.33(0.06)
0.60(0.04) 0.69(0.03)
0.78(0.03)
0.35(0.05)
-
Note: Standard errors in parentheses. PBC = Perceived Behavioral Control. The adults’ values (n=321) are shown above the diagonal, the students’ ones (n=208) below the diagonal. All coefficients are significant with p < .05.
The Cronbach’s coefficients were all satisfactory (Table 2). As regards the mean scores, the adults had a more favorable affective attitude toward the behavior, finding it more enjoyable and tastier, and perceiving it easier to be performed than the students. The adults showed a stronger intention to consume three portions of fresh fruit per day and reported that they had implemented this behavior in the past and in the two weeks before the second wave more often than students. Finally, the adults showed higher scores on self-identity as a healthy eater than the students. Conversely, the students perceived greater social pressure to carry out the behavior than the adults. The effect sizes of the significant group differences varied between the “very small” and “medium” thresholds (Cohen, 1988) (see Table 2).
Table 2 Reliability coefficients, descriptive statistics, groups differences on TPB constructs Adults (n=321) Constructs
Students (n=208)
Cronbach’s alpha
M (SD)
Cronbach’s alpha
M (SD)
t(527)
Cohen’s d
Affective Attitude
0.87
5.72 (1.03)
0.88
5.36 (1.05)
3.88***
0.35
Cognitive Attitude
0.82
6.26 (1.05)
0.79
6.30 (0.97)
n.s.
0.04
Subjective Norm
0.81
5.44 (1.09)
0.80
5.70 (1.00)
-2.85**
0.25
PBC
0.89
5.15 (1.53)
0.85
4.80 (1.42)
2.69**
0.24
11
Intention
0.95
4.80 (1.75)
0.94
4.37 (1.62)
2.83**
0.26
Past Behaviour
0.91
2.92 (1.23)
0.88
2.38 (1.07)
5.18***
0.47
Self-Identity
0.93
5.07 (1.32)
0.95
4.50 (1.35)
4.83***
0.43
Behaviour
0.93
3.09 (1.39)
0.91
2.42 (1.25)
5.68***
0.51
Note: PBC: Perceived Behavioral Control. ***p < .001, **p < .01.
3.1. Model Testing The original TPB model (Model 0) fitted the data well (Table 3). For the adults, it explained 79% of the intention variance and 49% of the fruit consuming behavior variance; for the students, it explained 73% and 56% of the variance, respectively. The standardized regression coefficients indicated that the intention of both adults and students was significantly associated with affective attitude (γadults = 0.46, p < .02; γstudents = 0.34, p < .02), subjective norm (γadults = 0.18, p < .001; γstudents = 0.35, p < .0001), and PBC (γadults = 0.66, p < .0001; γstudents = 0.52, p < .0001). Cognitive attitude was not related to intention (γadults = -0.26, ns; γstudents = -0.13, ns). PBC was the strongest predictor of intention in both samples. Behavior was affected only by intention (βadults = 0.66, p < .0001; βstudents = 0.71, p < .0001).
Table 3. Model fit indices Adults 0) TPB 1) TPB + PB 2) TPB + PB + SI Students 0) TPB 1) TPB + PB 2) TPB + PB + SI
χ2
df
p
CFI
RMSEA
SRMR
R2I
R2B
76.94 105.36 143.87
42 59 79
.0008 .0002 .0000
0.99 0.99 0.99
0.05 0.05 0.05
0.04 0.04 0.04
0.79 0.80 0.81
0.49 0.62 0.62
58.42 83.01 120.69
42 59 79
.0470 .0210 .0018
0.99 0.99 0.99
0.04 0.04 0.05
0.03 0.04 0.04
0.73 0.77 0.78
0.56 0.69 0.69 2 2 Note: TPB = classical TPB model; PB = Past Behavior; SI = Self-Identity; R I = Explained Intention variance; R B =
Explained Behavior variance.
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To establish whether past behavior and self-identity were able to improve the amount of explained variance of intention and behavior, two extended models were tested. Model 1 (Table 3) provided satisfactory fit indices but determined an improvement of explained intention variance of only 1% in the adults and of 4% in the students. Past behavior explained an additional 13% of behavior variance for both adults and students. In this model, subjective norm (γadults = 0.17, p < .0001; γstudents = 0.28, p < .0001), PBC (γadults = 0.52, p < .0001; γstudents = 0.41, p < .0001), and past behavior (γadults = 0.23, p < .002; γstudents = 0.22, p < .002) were significantly associated with intention, and affective (γadults = -0.14, ns; γstudents = 0.25, ns) and cognitive attitudes (γadults = 0.30, ns; γstudents = -0.03, ns) were not related to intention. Again, PBC was the strongest predictor of intention in both samples. Behavior was affected by intention (βadults = 0.39, p < .0001; βstudents = 0.49, p < .0001) and past behavior (βadults = 0.46, p < .0001; βstudents = 0.44, p < .0001). In the final extended model (Model 2), self-identity as a healthy eater explained an additional 1% of variance of intention of the adults and students in comparison with Model 1 but without improving the quota of the explained behavior variance. Subjective norm, PBC, past behavior, and self-identity were significantly related to intention (Figure 1). Behavior was affected only by intention and past behavior. The standardized indirect effects of subjective norm, PBC, self-identity, and past behavior on behavior through the mediation of intention were computed with LISREL. Only the indirect effects of subjective norm, PBC, and past behavior were significant (ps < .05); the coefficients were .05, .12, .09 for adults and .10, .12, and .11 for students, respectively. Finally, considering the final model, a multi-sample procedure with three consequential hypotheses was applied: a) patterns of factors and matrices equivalent (M0: Baseline model), b) invariance of factors loadings matrices (M1), and c) invariance of regression coefficients between exogenous and endogenous latent variables (M2). As none of the χ2 difference tests were significant (Table 4), we concluded in favor of model invariance across adult and student samples.
Table 4. 13
Multi-sample analysis Model Fit indices 2 M0 χ (158) = 264.56; p =.0000; CFI = 0.99 M1
χ2(166) = 278.88; p =.0000; CFI = 0.99
M2
χ2(176) = 284.61; p = .0000; CFI = 0.99
Test of invariance
M 1 − M 0: ∆χ2 (8) = 14.32, p > .05 ∆CFI = 0.00 M 2 − M 1: ∆χ2(10) = 5.73, p > .05 ∆CFI = 0.00
4. Discussion The findings of the present study suggest that the original TPB model has a strong explanatory value for daily fruit consumption both in the adult and student samples. The hypothesis that affective attitude is the most important attitudinal predictor was supported, consistent with the results of previous research in the field of food choice. The extended model that included past behavior accounted for additional quotas of variance, especially in the case of behavior. Past behavior and intention were positively associated with behavior. The inclusion of past behavior reduced the effect of affective component of attitude on intention to the non-significant level and therefore its indirect effect on behavior (Hagger et al., 2018). The inclusion of self-identity as a healthy eater in the final model, raised the quota of variance of intention only weakly. Self-identity was significantly associated with intention in both samples and, as hypothesized, acted as independent predictor of intention over and above TPB constructs when past behavior was controlled. This result is consistent with previous findings (Carfora et al., 2016; Caso et al., 2016; Rise et al., 2010). Individuals who perceived themselves as healthy eaters were more likely to develop the intention to consume three portions of fruits per day in the future. The unique effect of self-identity shows that the traditional antecedents of intention in the TPB, such as attitudes and subjective norm, do not account for self-related social influences on intention (Hagger & Chatzisarantis, 2006) and validate the inclusion of self-identity within the framework of the TPB 14
(Spark & Shepard, 1992). In addition, the examination of the relative effects of past behavior and self-identity demonstrated that, as expected, both self-identity and self-reported past behavior are important determinants of healthy intentions. Although self-identity might be inferred from past behavior (Fishbein & Ajzen, 2010), and thereby might act merely as a surrogate for the influence of behavioral habits, our results showed that the independent effect of self-identity persisted when past behavior is controlled. Consequently, this study further supports the proposal that the TPB should consider the role of self-identity in influencing behavioral intentions. However, Spark and Shepard (1992) and Spark (2000) identified several issues, especially related to the operationalization of the construct, which might instead measure attitude, past behavior or even intention. Recently, important suggestions about prototypical items to use for measuring self-identity over a wide range of behaviors and populations were provided by Snippe et al (2019). In conclusion, having a valid measure can be useful in order to decide if self-identity measures should be comprised or not in the socio-cognitive models like TPB. In the extended model, behavior was directly predicted by intention and past behavior, which had the strongest effects in both samples. The direct effect of past behavior on future behavior is coherent with our hypotheses and the results of previous studies (e.g. McEachan et al., 2011). However, contrary to our expectations, self-identity neither exerted direct nor indirect effects on behavior. This finding contrasts with the results of the meta-analysis by Rise et al. (2010), and the results of Caso et al. (2016), and Carfora et al. (2016). A reason for this inconsistency could be that self-identity measure refers to a general self-perception as a healthy eater, while the target behavior is very restricted. As argued by Dean et al. (2012), the discordancy between the two measures violates the principles of compatibility within the TPB framework (Ajzen, 1991), it may have had a role as regards our findings, and it should be carefully addressed in future studies. In both samples, PBC was the strongest predictor of intention, but did not directly influence behavior. However, in the literature, the overall evidence concerning the efficacy of PBC in directly 15
predicting behavior is not consistent (Godin et al., 2010), and our results are in line with previous studies on healthy eating (e.g. Carfora et al., 2016; Caso et al., 2016). Intention mediated the effects of PBC and subjective norm on behavior totally and the effect of past behavior only partially, thus indicating that consuming three servings of fruit per day is a behavior coming from both conscious deliberation and repeated and habitual behavior (for an extended discussion, see Hagger et al. 2018). Multi-sample analysis showed that the processes underlying intention formation and behavior execution are invariant between the two groups. However, some differences emerged in the mean scores, and, apart from subjective norm, students showed lower scores compared to adults. When projecting interventions to improve fruit consumption among young people, this evidence should be thoroughly considered. The current study has some inherent strengths: a) we differentiated affective and cognitive attitude, b) we analyzed self-identity effects when controlling for past behavior; c) we analyzed effects of TPB constructs, past behavior and self-identity on prospectively measured behavior; d) we examined the invariance of the processes for a specific behavior, in different groups and through multi-sample analyses; e) finally, to our knowledge few studies tested the TPB model for a specific healthy eating behavior – that is the consumption of three portions of fresh fruit –, and the current test significantly contribute to the body of evidence for the predictions specified in the model. Despite these points of strength, a number of potential limitations should be noted. We interviewed two convenience samples, with the student sample having a prevalence of women. Behavior was measured through self-report items, which could be subject to social desirability or social approval biases and retrieval inaccuracy. Nevertheless, this limitation is common in TPB-based studies on different eating behaviors and in those using more accurate self-reported measures such as a food diary (Miller, Abdel-Maksoud, Crane, Marcus, & Byers, 2008). A consequence of this limitation is that the effect of TPB constructs and other variables on objective behavior remains largely 16
unknown. Moreover, our study, although it considers a prospective measure of behavior, is correlational in design and does not allow the inference of causal relations.
4.1. Implications for Research and Practice From a theoretical point of view, our findings support the utility of decomposing attitude in affective and cognitive components, which show a differential relation with intention in the context of food choice. Such a finding suggests the importance of reinforcing the affective component of attitude, emphasizing the pleasantness and potential hedonic consequences of eating fruit in any intervention aimed to increase it. This point is also sustained by the fact that people may already be aware of the benefits of eating fruit, as shown by the high scores in the cognitive component of attitude, and a ceiling effect may have limited the predictive power of this construct (Blanchard et al., 2009a). PBC was the strongest predictor of intention in all models. To facilitate the perception of control, individuals must be supported to overcome obstacles and barriers (e.g., cost of fruit and difficulty in storage outside the home). Facilitating conditions (e.g., increasing the opportunities to buy ready-to-use fruit in cafeterias and vending machine) should be introduced to promote the development of intention to consume fruit. The research on this type of intervention is sparse. Nevertheless, a recent study on elementary classrooms showed that the presence of a refrigerator increases access to fresh fruit (Boehm, Schuknecht, & Cash, 2018). The importance of past behavior in predicting intention and behavior indicates that food consumption, although volitional and intentional, is almost certainly part of a consolidated food style built over time. Therefore, all educational programs aiming to consolidate fruit intake from a young age are very important. For our participants, a healthy eater identity was not predictive of the consumption of three servings of fresh fruit a day. This result suggests that the assessment of a general healthy eater self-identity may not be salient in consuming a specific amount of certain food groups (Brouwer & Mosack, 2015). In fact, for our participants, fruit consumption could not overlap completely with what it 17
means to eat healthy. For example, participants may have defined healthy eating in terms of calories restriction or avoiding fatty foods. In future research, measures designed to capture food-groupspecific identities (e.g., self-identity as a fruit eater) could improve the predictive ability of selfidentity with respect to the corresponding behavior (Carfora, Caso & Conner, 2017; Jung & Bice, 2019). In any case, more research is needed to understand the kind of consumption that defines a “healthy eating style”. Future research should also consider the possibility to assess other antecedents that may have an effect on healthy eating intention and behavior, such as anticipated affective reaction (e.g., regret for not having performed the behavior; Conner, 2018).
Acknowledgements The authors wish to thank Marta Da Dalt, Anna Sofia Fattor, Ilaria Inzaina, Libera Ylena Mastromatteo, Jacopo Noviello, Giulia Sovran, Dario Sperotto for their help in collecting and coding data for this research.
References Ajzen, I. (1991). The Theory of Planned Behavior: Some unresolved issues. Organizational Behaviour and Human Decision Processes, 50, 179-211. Armitage, C. J., & Conner, M. T. (1999). Distinguishing perceptions of control from self-efficacy: Predicting consumption of a low fat diet using the Theory of Planned Behavior. Journal of Applied Social Psychology, 29, 72-90. Bagozzi, R.P. (Ed.) (1994). Advanced Methods of Marketing Research. Oxford, UK: Blackwell Publisher. Bagozzi, R.P., & Edwards, J.R. (1998). A general approach for representing constructs in organizational research. Organizational Research Methods, 1, 45–87. Blanchard, C.M., Fisher, J., Sparling, P.B., Shanks, T.H., Nehl, E., Rhodes, R.E., Courneya, K.S., & Baker, F. (2009a). Understanding adherence to 5 servings of fruits and vegetables per day: a theory of planned behavior perspective. Journal of Nutrition Education and Behavior, 41, 3-10.
18
Blanchard, C.M., Kupperman, J., Sparling, P.B., Nehl, E., Rhodes, R.E., Courneya, K.S., & Baker, F. (2009b). Do ethnicity and gender matter when using the theory of planned behavior to understand fruit and vegetable consumption? Appetite, 52, 15–20. Boehm, R., Schuknecht, T.J., & Cash, S.B. (2018). Cool Intentions: Adding Refrigerators to Elementary Classrooms Improves Fruit and Vegetable Access in a Pilot Study. Journal of Nutrition Education and Behavior, 50, 746-747. Brouwer, A.M., & Mosack, K.E. (2015). Expanding the theory of planned behavior to predict healthy eating behaviors. Nutrition & Food Science, 45, 39-53. Byrne, B.M. (1998). Structural Equation Modeling with LISREL, PRELIS and SIMPLIS: Basic Concepts, Applications and Programming. Mahwah, NJ: Lawrence Erlbaum Associates. Canova, L., & Manganelli, A.M. (2016). Fruit and vegetables consumption as snacks among young people. The role of descriptive norm and habit in the Theory of Planned Behavior. TPM: Testing, Psychometrics, Methodology in Applied Psychology, 23, 83-97. Carfora, V., Caso, D., & Conner, M. (2016). The role of self-identity in predicting fruit and vegetable intake. Appetite, 106, 23-29. Carfora, V., Caso, D., & Conner, M. (2017). Correlational study and randomised controlled trial for understanding and changing red meat consumption: The role of eating identities. Social Science & Medicine, 175, 244-252. Caso, D., Carfora, V., & Conner, M. (2016). Predicting intentions and consumption of fruit and vegetables in Italian adolescents. Effects of anticipated regret and self-identity. Psicologia sociale, 3, 319-326. Cohen J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. Conner, M. (2018). Experiential attitude and anticipated affect. In: Williams, D.M., Rhodes, R.E., Conner, M. (Eds.), Affective Determinants of Health-related Behavior. New York: Oxford University Press. Conner, M. T., Norman, P., & Bell, R. (2002). The Theory of Planned Behavior and healthy eating. Health Psychology, 21, 194-201. Conner, M., & Armitage, C. J. (1998). Extending the theory of planned behaviour: A review and avenues for further research. Journal of Applied Social Psychology, 28, 1430–1464. Conner, M., & McMillan, B. (1999). Interaction effects in the theory of planned behavior: Studying cannabis use. British Journal of Social Psychology, 38, 195-222.
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Dean, M., Raats, M.M., & Shepherd, R. (2012). The role of self-identity, past behaviour and the interaction in predicting intention to purchase fresh and processed organic food. Journal of Applied Social Psychology, 42, 669–688. Dunn, K.I., Mohr, P, Wilson, C.J., & Wittert G.A. (2011). Determinants of fast-food consumption. An application of the Theory of Planned Behaviour. Appetite, 57, 349–357. Eagly, A.H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich. Fishbein, M., & Ajzen, I. (2010). Predicting and Changing Behavior: The Reasoned Action Approach. New York: Psychology Press. Godin, G., Amireault, S., Bélanger-Gravel, A., Vohl, M. C., Pérusse L., & Guillaumie, L. (2010). Prediction of daily fruit and vegetable consumption among overweight and obese individuals. Appetite, 54, 480-484. Hagger, M.S., & Chatzisarantis, N.L.D. (2006). Self-identity and the theory of planned behaviour: Between- and within-participants analyses. British Journal of Social Psychology, 45, 731-757. Hagger, M.S., Polet, J., & Lintunen, T. (2018). The reasoned action approach applied to health behavior: Role of past behavior and test of some key moderators using metaanalytic structural equation modeling. Social Science & Medicine, 213, 85–94. Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. Istituto superiore di sanità. EpiCentro, Il portale dell'epidemiologia per la sanità pubblica. Centro nazionale per la prevenzione delle malattie e la promozione della salute [National Institute of Health. EpiCentro, the portal of epidemiology for public health. National Center for Disease Prevention and Health Promotion], (2019). http://www.epicentro.iss.it/passi/dati/frutta#dati. Accessed 15 February 2019. Jung, S.E., & Bice, C. (2019). The Role of Self-Identity in Predicting College Students' Intention to Consume Fruits and Vegetables. Journal of Nutrition Education and Behavior, 51, 173-181. McEachan, R.R.C., Conner, M., Taylor, N.J., & Lawton, R. (2011). Prospective prediction of health-related behaviors with the Theory of Planned Behavior: A meta-analysis. Health Psychology Review, 5, 97-144. McEachan, R.R.C., Taylor, N., Harrison, R., Lawton, R., Gardner, P., & Conner, M. (2016). Metaanalysis of the Reasoned Action Approach (RAA) to understanding health behaviors. Annals of Behavioral Medicine, 50, 592-612.
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Miller T.M., Abdel-Maksoud, M.F., Crane, L.A., Marcus, A.C., & Byers, T.E. (2008). Effects of social approval bias on self-reported fruit and vegetable consumption: a randomized controlled trial. Nutrition Journal, 7:118. Reid, M., Sparks, P., & Jessop, D.C. (2018). The effect of self-identity alongside perceived importance within the theory of planned behaviour. European Journal of Social Psychology, 48, 883-889. Rhodes, R. E., Blanchard, C. M., & Matheson, D. H. (2006). A multicomponent model of the theoryof planned behaviour. British Journal of Health Psychology,11, 119–137. Ries, F., Hein, V., Pihu, M., & Armenta, J.M.S. (2012). Self-identity as a component of the theory of planned behaviour in predicting physical activity. European Physical Education Review, 18, 322–334. Rise, J., Sheeran, P., & Hukkelberg, S. (2010). The role of self-identity in the theory of planned behavior: A meta-analysis. Journal of Applied Social Psychology, 40, 1085–1105. Snippe, M. H. M., Peters, G. J., & Kok, G. (2019, January 23). The Operationalization of SelfIdentity in Reasoned Action Models: A Systematic Review of Self-Identity Operationalizations in Three Decades of Research. https://doi.org/10.31234/osf.io/ygpmw Sparks, P. (2000). Subjective expected utility-based attitude–behavior models: The utility of selfidentity. In D. J. Terry & M. A. Hogg (Eds.), Attitudes, behavior, and social context: The role of norms and group membership (pp. 31–46). Hillsdale, NJ: Lawrence Erlbaum. Sparks, P., & Shepherd, R. (1992). Self-identity and the theory of planned behavior—assessing the role of identification with green consumerism. Social Psychology Quarterly, 55, 388–399. Sparks, P., Shepherd, R., Wiering, N., & Zimmermanns, N. (1995). Perceived behavioural control, unrealistic optimism and dietary change: An exploratory study. Appetite, 24, 243-255. Stryker, S. (1987). Identity theory: Developments and extensions. In K. Yardley & T. Honess (Eds.), Self and identity, pp. 89-104. New York: Wiley. Stryker, S., & Burke, P.J. (2000). The past, present, and future of an identity theory. Social Psychology Quarterly, 63, 284-297. Terry, D.J., Hogg, M.A., & White, K.M. (1999).The theory of planned behaviour: Self-identity, social identity, and group norms. British Journal of Social Psychology, 38, 225–244. WHO (World Health Organization). Diet, nutrition and the prevention of chronic diseases. Report of a Joint/WHO/FAO Expert Consultation. (2003). https://www.who.int/dietphysicalactivity/publications/trs916/download/en/. Accessed 15 February 2019.
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Figure 1. Standardized path coefficients in the adult and student samples
Affective Attitude
Cognitive Attitude
Self-Identity .11* .13*
Intention
.28* .39*
Behavior
Subjective Norm
PBC
Past Behavior
Note: PBC: Perceived Behavioral Control. The first coefficients refer to the adult sample and the second ones to the student sample. * p < .05
Predicting Fruit Consumption: A multi-group application of the Theory of Planned Behavior
Highlights -
A TPB-based two-wave multi-group study is performed.
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The TPB framework is extended with measures of past behavior and self-identity.
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Both the affective and the evaluative component of attitude are assessed.
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Past behavior but not self-identity shows a significant link with future behavior.
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The relation model fits the data well and proves to be invariant across groups.
S0195-6663(19)30754-8
DOI:
https://doi.org/10.1016/j.appet.2019.104490
Reference:
APPET 104490
To appear in:
Appetite
Received Date: 10 June 2019 Revised Date:
19 September 2019
Accepted Date: 12 October 2019
Please cite this article as: Canova L., Bobbio A. & Manganelli A.M., Predicting fruit consumption: A multi-group application of the Theory of Planned Behavior, Appetite (2019), doi: https://doi.org/10.1016/ j.appet.2019.104490. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Predicting Fruit Consumption: A multi-group application of the Theory of Planned Behavior
Authors: Luigina Canova, PhD, Andrea Bobbio, PhD, Anna Maria Manganelli Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Italy
Corresponding Author Luigina Canova, Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Via Venezia 14, 35131, Italy. Phone : +390498276686, Fax: +39 0498274719, E-mail: [email protected]
Conflict of Interest Disclousure: The authors have no conflict of interest.
The research project was funded by the University of Padua: ‘Ex 60%’ Scientific Research Funds – 60A17-3217/12.
Predicting Fruit Consumption: A multi-group application of the Theory of Planned Behavior
ABSTRACT The main aim of the study was to test a two-wave longitudinal prediction model for the consumption of at least three portions of fruit per day, that was grounded on the Theory of Planned Behaviour (TPB; Ajzen, 1991), extended with measures of past behaviour and self-identity as a healthy eater. Self-identity is defined as a salient part of the self-concept specifically relates to a desirable behaviour, such as, in this case, healthy eating. A second aim of the study was to test the invariance of the proposed model in two samples of Italian university students (n=208) and no-student adults (n=321). At time 1 the questionnaire was made up of measures of TPB constructs, with the addition of past consumption and self-identity as a healthy eater. Both the affective and evaluative components of attitude were assessed. At time 2, only the target behaviour was surveyed. After checking both adequacy of the measurement model and reliability estimates, data were analysed via structural equation modelling that returned good fit indices. Results showed that intention was positively associated with subjective norm, perceived behavioural control, self-identity and past behaviour. Altogether, they explained 78% of the intention variance in the student group, and 81% in the adult group. After controlling for intention, past behaviour – but not self-identity – was significantly related to the selfreported behaviour, and the model explained 69% and 62% of behaviour variance, respectively. Multisample analysis supported model invariance across the two groups. Results and their possible applications are presented and discussed.
Predicting Fruit Consumption: A multi-group application of the Theory of Planned Behavior
1. Introduction Fruit and vegetables are important components of a balanced diet. A joint report of the World Health Organization and the Food and Agriculture Organization (WHO, 2003) recommends the consumption of at least 400 grams of fruit and vegetables per day as a minimal quantity to prevent chronic illnesses and obesity. This amount corresponds to three portions of fruit and two portions of vegetables. Despite the well-known evidence that fruit and vegetables are key components of a healthy diet, in Italy, between 2014 and 2017, only 10% of those interviewed aged 18–69 years ate five portions of fruit and vegetables per day (Istituto Superiore di Sanità, 2019). Consumption increases with age, is more frequent among women, and among wealthier and more educated people. The present study focused on the consumption of three portions of fresh fruit per day and aimed to identify the determinants of this behavior in young and adult people. The Theory of Planned Behavior (TPB; Ajzen, 1991) was assumed as theoretical reference model. According to TPB, the immediate antecedent of a behavior is intention to perform it. Intention is influenced by attitude toward the behavior, subjective norm, and perceived behavioral control (PBC). PBC contributes to the prediction of both intention and behavior. The TPB has been already successfully applied to a wide range of behaviors including healthy eating (e.g., Conner, Norman, & Bell, 2002; McEachan, Conner, Taylor, & Lawton, 2011), eating dietary behaviors (e.g,. Armitage & Conner, 1999; Brouwer & Mosack, 2015), fruit and vegetables consumption (e.g., Blanchard et al., 2009a; Blanchard et al., 2009b; Godin, Amireault, Bélanger-Gravel, Vohl, Pérusse, Guillaumie, 2010). The present research, conducted in the Italian context, analyzes the efficacy of TPB in the prediction of both intentions and action for a specific behavior, which is consuming three servings of fresh fruit per day.
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The original TPB model has been extended by decomposing some constructs in two or more facets or by exploring a number of additional predictors. In the model hypothesized in this study, we distinguished two attitude components (affective and cognitive) and considered two additional predictors: past behavior and self-identity. As regards attitude, the affective component reflects the emotion-based judgments about the behavior; the cognitive component represents its anticipated consequences, benefits, or costs. Previous studies showed that affective attitude was the dominant attitudinal predictor of intention in the case of healthy behaviors (McEachan et al., 2016) and of the consumption of five servings of fruit and vegetables a day (Blanchard et al., 2009a; Blanchard et al., 2009b). Regarding additional predictors, a number of studies considered past behavior (e.g., Carfora, Caso, & Conner, 2016; Hagger, Polet, & Lintunen, 2018) and self-identity (e.g., Carfora et al., 2016; Rise, Sheeran, & Hukkelberg, 2010; Reid, Sparks, & Jessop, 2018). The inclusion of past behavior within TPB is especially justified when the behavior is seen to be influenced by habit (Conner & Armitage, 1998). In fact, the repeated performance of a behavior, such as in the case of fruit consumption, transfers it from a reasoned process, as described by TPB, to an automatic process (Conner & McMillan, 1999). The contribution of past behavior to the prediction of intention and behavior when TPB constructs are controlled is of particular interest. For example, McEachan et al.’s (2011) meta-analysis showed that past behavior increased the quotas of explained variance in healthy intentions and behaviors. However, a recent meta-analysis by Hagger et al. (2018) showed that the inclusion of past-behavior resulted in a significant attenuation of the direct effect of intention on behavior and of the indirect effects of the other TPB constructs on behavior. Self-identity, or “the salient part of the actor’s self which relates to a particular behavior” (Conner & Armitage, 1998, p. 1444), can be considered as a set of enduring characteristics that people ascribe to themselves (e.g. “I think of myself as a green consumer”; Sparks, 2000). According to identity theories (Stryker, 1987), people use several context-dependent socially meaningful 2
categories in describing themselves in terms of, for example, sociodemographic characteristics (e.g. gender), social roles (e.g. mother, father), social types (e.g. smoker, healthy eater, blood donor, green consumer). Specific expectations associated with these categories guide people's motivation and behavior, so that people usually plan and act in accordance with their self-identity. Fishbein and Ajzen (2010) argued that self-identity overlaps conceptually with past behavior, as people could infer selfidentities through the examination of past behaviors. If this is the case, self-identity should not have an independent effect on intention when past behavior is accounted for. Moreover, according to attitude theorists (Eagly & Chaiken, 1993), self-identity also shows a conceptual overlap with attitude. Despite ongoing debates, the findings of a meta-analytic study by Rise et al. (2010) showed that self-identity explained additional variance in intentions and that its effects were independent from both past behavior and other TPB constructs. Other studies in a number of behavioral contexts (Dean, Raats, & Shepherd, 2012; Dunn, Mohr, Wilson, & Witterta, 2011; Jung & Bice, 2019; Ries, Hein, Pihu, & Armenta, 2012; Reid et al., 2018) supported the conclusions of this meta-analysis. In addition, a subgroup of studies considered in the meta-analysis of Rise et al. (2010) included a prospective measure of behavior; the inclusion of self-identity engendered a significant increase in the explained behavior variance; and self-identity had a direct influence on behavior even after intention and PBC had been taken into account. However only a few studies examined the effect of self-identity on behavior when past behavior is controlled (for a review see Carfora et al., 2016). In the field of food choices, self-identity as a healthy eater is often considered. When individuals see themselves as a healthy eater, they should regulate their behavior consistently with both expectations and activities associated with being a healthy eater. Brouwer and Mosack (2015) found that the healthy eater identity predicted the intention to eat a healthy diet (i.e., low-fat dairy, fruit, vegetables, and whole grains) and that it was a significant predictor of low-fat dairy and fruit but not of vegetables and whole grains consumption. In the Italian context, two studies (Carfora et al., 3
2016; Caso, Carfora, & Conner, 2016) on adolescents’ and university students’ eating five portions of fruit and vegetables a day showed that self-identity as a healthy eater had direct effects both on intention and behavior after controlling for past behavior.
1.1. Aims of the study In sum, the first aim of the present study was to test the validity of a TPB model extended with past behavior and self-identity as a healthy eater applied to the consumption of three portions of fresh fruit per day. As mentioned before, we distinguished two attitude components: cognitive and affective. Based on previous research, we expected only the affective component of attitude to be associated with intention and, indirectly, with behavior. We predicted also that, after controlling for TPB constructs, past behavior and self-identity would account for additional variance of intention and behavior and affect them both. Direct effects of past behavior on future behavior may reflect non-conscious processes, while indirect effects through social cognitive variables may represent reasoned processes. When the target behavior is expected to be performed frequently, such as in the case of daily fruit consumption, thus offering greater opportunity for habit formation, it is probable that past behavior would exert larger direct effects than intentions on future behavior (Hagger et al., 2018). In addition, self-identity may influence directly the behavior because it produces a standard of reference for it (Stryker & Burke, 2000), allowing individuals to assess the perceived overlapping between their identities and the meanings of behavior. The second aim concerned the generality of the processes leading to intention formation and, ultimately, to behavior in different categories of individuals, that is, Italian university students and non-student adults. Research considering the type of sample as a moderator of the relationships within TPB are rare. In two meta-analyses by McEachan et al. (2016) and McEachan et al. (2011) the comparisons between three different age groups (adults, university students, and adolescents) showed that, in the case of dietary behaviors, the differences in the strength of the relationships 4
between attitude, norms, PBC, and intention emerged only in adolescents/school-aged versus adult samples. However, in these meta-analyses the range of behaviors taken into account was very broad, going from “healthy eating” – a general target – to the consumption of “five portions of fruit and vegetables” – which is, on the contrary, much narrowed. Instead, to our knowledge the number of studies that aimed to test the invariance of the processes leading to the formation of intention and behavior in the case of specific behaviors, considering different groups within the same population or different cultural contexts, are very scarce (an exception is represented by Ries et al., 2012, which tested the invariance of a TPB extended model among Spanish and Estonian students samples). Supporting the invariance of the over mentioned processes might be of great interest in order to design intervention programs which, at the same time, may target different subgroups of a population. Based on the results of the meta-analyses mentioned above, we expected to confirm invariance of the processes leading to the formation of intention and then to behavior in both university students and adults.
2. Method 2.1. Procedure and Participants The participants completed a structured questionnaire at time 1 (T1), including the measures of the extended TPB model and socio-demographic variables. Two weeks later (T2), the participants’ selfreported behavior measures were collected. The relatively short follow-up was chosen mainly because short time lag (i.e., one or two weeks) is often used when the target behavior is performed frequently, or perhaps daily, as in the case of eating behaviors or making regular exercise (e.g. Blanchard et al., 2009a; Blanchard et al., 2009b; Brower & Mosack, 2015; Caso et al., 2016; Carfora et al., 2016; Conner & Armitage, 1998; Rhodes, Blanchard & Matheson, 2006). Moreover, the short short time lag was preferred in order to reflect optimal predictive accuracy of the model – particularly for the intention-behavior relationship, which could be attenuated by a longer follow-up 5
(McEachan et al., 2011) allowing other factors to change intentions – and to better respect the TPB tenets of time, context, target, and action (Ajzen, 1991). On the contrary, self-identity, which is defined as an individual enduring disposition, is less likely to be subject to significant changes within two weeks. For the adult sample, 100 university students from two courses in Social Psychology at Padua University were engaged in data collection as part of a research–experience assignment. The psychology students were asked to administer the questionnaire to five of their acquaintances or neighbors. All students were trained to deliver an informed consent form that participants had to sign and return before completing the questionnaire at time 1. Informed consent forms and completed questionnaires were collected in separate envelopes, and then returned to the researchers by the students. The participants were informed about the aim of the study, the duration of the task, and the possibility of withholding their consent to participate, and they were assured that all responses would remain confidential. They filled in the questionnaire autonomously and gave it back immediately. Two weeks later, they received the second questionnaire and were quickly debriefed. Among the 500 potential participants, usable data came from 417 individuals (response rate: 83%). Among these, 376 also completed the second questionnaire (final response rate: 75%). In order to obtain a homogeneous sample with regard to eating style, only the participants who declared to be omnivorous were retained. The final adult sample (n = 321) was comprised of 167 women (52%) and 151 men (47%) (3 missing data), aged 37–79 years (M = 53, SD = 7), and mostly lived in Northeast Italy (68%). Two hundred and forty-seven were in the workforce (77%) and 73 (23%) were out of work (housewife, retired, unemployed). In terms of education, 33% completed compulsory schools, 44% reached the high school level, and 21% had a university degree (7 missing data). The mean body mass index (BMI) was 25.6 (SD = 3.3) for men and 23.4 (SD = 3.5) for women.
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As regards the student sample, all of the participants were from Padua University and did not receive course credits or other form of reward for their participation. None of these participants was involved in recruiting the adult sample. The students filled in the questionnaires during regular classes. Among the 308 students responding at T1, a total of 255 (response rate: 82.8%) returned the second questionnaire two weeks later. Our final sample (n=208) was made up of 164 women (79%) and 42 men (20%) (2 missing data) who followed an omnivorous diet. The majority (n = 114, 54.8%) attended the degree course in Sociology, 83 (39.9%) in Education, and 8 (3.8%) attended other courses (3 missing data). The mean age was 19.8 years (SD = 1.2; range: 18–28), and the mean BMI was 22.5 (SD =3.3) for men and 21 (SD = 3) for women. Almost all lived in Northeast Italy (96.2%). The research project obtained approval from the Ethical Committee of the School of Psychology at Padua University (code 2173).
2.2. Measures The questionnaires for adults and students presented the same measures of TPB constructs already used in previous studies in the Italian context (Canova & Manganelli, 2016), that followed the TPB questionnaire construction guidelines (Fishbein & Ajzen, 2010). The target behavior was the consumption of at least three portions of fresh fruit per day in the next two weeks. For all participants the following description was provided: “a portion corresponds to 150 grams of fresh fruit: a medium-sized fruit such as an apple, pear, etc.; two small fruits such as plums, apricots, etc.; and a glass of fresh fruit juice.” In the first wave of the study (T1), participants were asked to report their attitudes, subjective norm, perceived behavioral control, past behavior, self-identity, and demographics information. Attitude towards the Behavior. Attitude was measured by presenting the participants the statement: “to consume at least three portions of fresh fruit per day in the next two weeks would be....”, and 7
asking them to respond on eleven 7-point semantic differential adjective scales that tapped into both cognitive (harmful–beneficial, negative–positive, stupid–intelligent, unhealthy–healthy) and affective (sad–joyful, bad–good, undesirable–desirable, depreciable–appreciable, disagreeable– agreeable, distasteful–tasteful, unpleasant–pleasant) components. The response scales were anchored from 1 (negative pole) to 7 (positive pole). Subjective Norm. The participants were asked to respond on a 7-point Likert scale to four items such as “Most people who are important to me would like me to consume at least three portions of fresh fruit per day in the next two weeks.” The anchors varied for each question, but all lower points (i.e., 1) reflected low agreement, approval, or support, and all higher points (i.e., 7) reflected high agreement, approval, and support. Perceived Behavioral Control. This was measured with four items such as “To what extent do you think consuming at least three portions of fresh fruit per day over the next two weeks is a behavior under your control?” The 7-point response scale presented different anchors for each item with lower points (i.e., 1), which indicate low agreement, easiness, and ability of control, and higher points (i.e., 7), which indicate high agreement, easiness, and ability. Intention. Four items were used such as: “I intend to consume at least three portions of fresh fruit per day over the next two weeks.” Lower points on the response scale (i.e., 1) indicated low agreement, strength of intention, and likelihood, whereas higher points (i.e., 7) indicated higher agreement, strength of intention, and likelihood. Past behavior. The items were as follows: “In the last two weeks, have you consumed at least three servings of fresh fruit a day?”; “In the last two weeks, how often did you consume at least three portions of fresh fruit a day?”; and “In the last two weeks, how many servings of fresh fruit have you consumed per day?” The response scale ranged from 1 (never/no one) to 5 (regularly/everyday/four or more). Self-identity as a healthy eater. To measure self-identity as a healthy eater, eight items derived from the literature were adopted (Carfora et al., 2016; Sparks & Shepherd, 1992; Spark, Shepard, 8
Wieringa & Zimmermanns, 1995; Terry, Hogg, & White, 1999). Example statements included: “I think of myself as a healthy eater”, “I think of myself as a person who is interested in eating healthy”, “I think of myself as a someone who is concerned about the health consequences of what I eat”, “I see myself as someone who eats healthy”, “I am the kind of person who eats healthy”, “Eating healthy is an important part of who I am.” Four of the items used were very similar to the seven prototypical items recently proposed by Snippe, Peters and Kok (2019) as a way to measure self-identity in a wide range of behaviors. The response scale ranged from 1 (strongly disagree) to 7 (strongly agree). Demographics. Gender, age, geographic area of residence, education (or, in the case of student, the course of study), employment status, weight, and height were assessed. Moreover, the participants were asked, “How would you define your diet?” (Omnivorous; Vegetarian; Vegan; Omnivorous, but I follow limitations for health reasons; I follow a kosher/halal regime or other limitations for religious reasons). At T2, the participants had to report their consumption behavior with reference to the two weeks immediately before the second wave of the research. For this purpose, the first two items that captured past behavior at T1 were used again. In particular: “In the last two weeks, have you consumed at least three servings of fresh fruit a day?” with a response scale ranging from 1 (No, never) to 5 (Yes, regularly), and “In the last two weeks, how often did you consume at least three portions of fresh fruit a day?” with a response scale from 1 (never) to 5 (everyday). All measures reliability statistics for the two samples are reported in Table 2.
2.3. Data analysis In order to check whether all measures detected distinct constructs, the goodness-of-fit of the measurement models was assessed via Confirmatory Factor Analysis. Analyses were performed 9
using the maximum likelihood method applied to covariance matrices (LISREL 8.80). Two parcels were created when the number of indicators was greater than two (Bagozzi & Edwards, 1998). Structural equation modeling were estimated to test the hypothesized models of relations. Goodness-of-fit was evaluated by means of χ2, the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). A satisfactory model fit is indicated by χ2 not significant even if it strongly depends on sample size; by CFI values greater than or equal to 0.95, by RMSEA values equal to or lower than 0.06, and by SRMR values equal to or lower than 0.08 (Hu & Bentler, 1999). Cronbach's alpha coefficients, descriptive statistics, and the differences between the mean scores of adults and students were computed for all the variables (t test and Cohen's d). Finally, the multi-sample procedure was used to test whether some or all of the parameters of the model were invariant across groups (Byrne, 1998).
3. Results A measurement model with 8 correlated latent factors and 16 indicators was tested in both samples separately. The goodness-of-fit indices were satisfactory: adult sample, χ2(76) = 137.55, p = .0001, RMSEA = 0.05, CFI = 0.99, SRMR = 0.04; student sample, χ2(76) = 120.46, p = .0008, RMSEA = 0.05, CFI = 0.99, SRMR = 0.04. The standardized factor loadings, which were all significant, showed that each latent factor was defined by its own indicators. The correlations among the latent factors (Table 1) were all significant, and the 95% confidence intervals, which were obtained by considering two standard errors above and below the coefficients, did not include the perfect correlation (i.e., 1.00), thus supporting the fact that all measures captured the distinct constructs in both samples (Bagozzi, 1994).
Table 1. Correlations between TPB latent factors 10
Constructs
Affective Cognitive Attitude Attitude
Subjective Norm
PBC
Intention
Past Behaviour
SelfIdentity
Behaviour
Affective Attitude Cognitive Attitude Subjective Norm
-
0.80(0.05)
0.33(0.07)
0.57(0.06) 0.65(0.05)
0.55(0.06)
0.32(0.07) 0.49(0.06)
0.89(0.04)
-
0.41(0.08)
0.36(0.08) 0.48(0.07)
0.27(0.08)
0.19(0.08) 0.28(0.08)
0.45(0.06) 0.48(0.06)
-
0.23(0.08) 0.53(0.06)
0.35(0.08)
0.21(0.08) 0.40(0.07)
PBC
0.48(0.05) 0.37(0.06)
0.29(0.06)
-
0.74(0.04)
0.72(0.05)
0.53(0.06) 0.58(0.05)
Intention
0.62(0.04) 0.48(0.06)
0.45(0.05)
0.82(0.02)
-
0.77(0.04)
0.57(0.06) 0.75(0.04)
Past Behaviour 0.54(0.05) 0.34(0.07)
0.26(0.06)
0.76(0.03) 0.79(0.03)
-
0.56(0.06) 0.80(0.04)
Self-Identity
0.45(0.06) 0.28(0.07)
0.21(0.06)
0.47(0.05) 0.52(0.05)
0.38(0.06)
-
0.50(0.06)
Behaviour
0.44(0.05) 0.25(0.06)
0.33(0.06)
0.60(0.04) 0.69(0.03)
0.78(0.03)
0.35(0.05)
-
Note: Standard errors in parentheses. PBC = Perceived Behavioral Control. The adults’ values (n=321) are shown above the diagonal, the students’ ones (n=208) below the diagonal. All coefficients are significant with p < .05.
The Cronbach’s coefficients were all satisfactory (Table 2). As regards the mean scores, the adults had a more favorable affective attitude toward the behavior, finding it more enjoyable and tastier, and perceiving it easier to be performed than the students. The adults showed a stronger intention to consume three portions of fresh fruit per day and reported that they had implemented this behavior in the past and in the two weeks before the second wave more often than students. Finally, the adults showed higher scores on self-identity as a healthy eater than the students. Conversely, the students perceived greater social pressure to carry out the behavior than the adults. The effect sizes of the significant group differences varied between the “very small” and “medium” thresholds (Cohen, 1988) (see Table 2).
Table 2 Reliability coefficients, descriptive statistics, groups differences on TPB constructs Adults (n=321) Constructs
Students (n=208)
Cronbach’s alpha
M (SD)
Cronbach’s alpha
M (SD)
t(527)
Cohen’s d
Affective Attitude
0.87
5.72 (1.03)
0.88
5.36 (1.05)
3.88***
0.35
Cognitive Attitude
0.82
6.26 (1.05)
0.79
6.30 (0.97)
n.s.
0.04
Subjective Norm
0.81
5.44 (1.09)
0.80
5.70 (1.00)
-2.85**
0.25
PBC
0.89
5.15 (1.53)
0.85
4.80 (1.42)
2.69**
0.24
11
Intention
0.95
4.80 (1.75)
0.94
4.37 (1.62)
2.83**
0.26
Past Behaviour
0.91
2.92 (1.23)
0.88
2.38 (1.07)
5.18***
0.47
Self-Identity
0.93
5.07 (1.32)
0.95
4.50 (1.35)
4.83***
0.43
Behaviour
0.93
3.09 (1.39)
0.91
2.42 (1.25)
5.68***
0.51
Note: PBC: Perceived Behavioral Control. ***p < .001, **p < .01.
3.1. Model Testing The original TPB model (Model 0) fitted the data well (Table 3). For the adults, it explained 79% of the intention variance and 49% of the fruit consuming behavior variance; for the students, it explained 73% and 56% of the variance, respectively. The standardized regression coefficients indicated that the intention of both adults and students was significantly associated with affective attitude (γadults = 0.46, p < .02; γstudents = 0.34, p < .02), subjective norm (γadults = 0.18, p < .001; γstudents = 0.35, p < .0001), and PBC (γadults = 0.66, p < .0001; γstudents = 0.52, p < .0001). Cognitive attitude was not related to intention (γadults = -0.26, ns; γstudents = -0.13, ns). PBC was the strongest predictor of intention in both samples. Behavior was affected only by intention (βadults = 0.66, p < .0001; βstudents = 0.71, p < .0001).
Table 3. Model fit indices Adults 0) TPB 1) TPB + PB 2) TPB + PB + SI Students 0) TPB 1) TPB + PB 2) TPB + PB + SI
χ2
df
p
CFI
RMSEA
SRMR
R2I
R2B
76.94 105.36 143.87
42 59 79
.0008 .0002 .0000
0.99 0.99 0.99
0.05 0.05 0.05
0.04 0.04 0.04
0.79 0.80 0.81
0.49 0.62 0.62
58.42 83.01 120.69
42 59 79
.0470 .0210 .0018
0.99 0.99 0.99
0.04 0.04 0.05
0.03 0.04 0.04
0.73 0.77 0.78
0.56 0.69 0.69 2 2 Note: TPB = classical TPB model; PB = Past Behavior; SI = Self-Identity; R I = Explained Intention variance; R B =
Explained Behavior variance.
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To establish whether past behavior and self-identity were able to improve the amount of explained variance of intention and behavior, two extended models were tested. Model 1 (Table 3) provided satisfactory fit indices but determined an improvement of explained intention variance of only 1% in the adults and of 4% in the students. Past behavior explained an additional 13% of behavior variance for both adults and students. In this model, subjective norm (γadults = 0.17, p < .0001; γstudents = 0.28, p < .0001), PBC (γadults = 0.52, p < .0001; γstudents = 0.41, p < .0001), and past behavior (γadults = 0.23, p < .002; γstudents = 0.22, p < .002) were significantly associated with intention, and affective (γadults = -0.14, ns; γstudents = 0.25, ns) and cognitive attitudes (γadults = 0.30, ns; γstudents = -0.03, ns) were not related to intention. Again, PBC was the strongest predictor of intention in both samples. Behavior was affected by intention (βadults = 0.39, p < .0001; βstudents = 0.49, p < .0001) and past behavior (βadults = 0.46, p < .0001; βstudents = 0.44, p < .0001). In the final extended model (Model 2), self-identity as a healthy eater explained an additional 1% of variance of intention of the adults and students in comparison with Model 1 but without improving the quota of the explained behavior variance. Subjective norm, PBC, past behavior, and self-identity were significantly related to intention (Figure 1). Behavior was affected only by intention and past behavior. The standardized indirect effects of subjective norm, PBC, self-identity, and past behavior on behavior through the mediation of intention were computed with LISREL. Only the indirect effects of subjective norm, PBC, and past behavior were significant (ps < .05); the coefficients were .05, .12, .09 for adults and .10, .12, and .11 for students, respectively. Finally, considering the final model, a multi-sample procedure with three consequential hypotheses was applied: a) patterns of factors and matrices equivalent (M0: Baseline model), b) invariance of factors loadings matrices (M1), and c) invariance of regression coefficients between exogenous and endogenous latent variables (M2). As none of the χ2 difference tests were significant (Table 4), we concluded in favor of model invariance across adult and student samples.
Table 4. 13
Multi-sample analysis Model Fit indices 2 M0 χ (158) = 264.56; p =.0000; CFI = 0.99 M1
χ2(166) = 278.88; p =.0000; CFI = 0.99
M2
χ2(176) = 284.61; p = .0000; CFI = 0.99
Test of invariance
M 1 − M 0: ∆χ2 (8) = 14.32, p > .05 ∆CFI = 0.00 M 2 − M 1: ∆χ2(10) = 5.73, p > .05 ∆CFI = 0.00
4. Discussion The findings of the present study suggest that the original TPB model has a strong explanatory value for daily fruit consumption both in the adult and student samples. The hypothesis that affective attitude is the most important attitudinal predictor was supported, consistent with the results of previous research in the field of food choice. The extended model that included past behavior accounted for additional quotas of variance, especially in the case of behavior. Past behavior and intention were positively associated with behavior. The inclusion of past behavior reduced the effect of affective component of attitude on intention to the non-significant level and therefore its indirect effect on behavior (Hagger et al., 2018). The inclusion of self-identity as a healthy eater in the final model, raised the quota of variance of intention only weakly. Self-identity was significantly associated with intention in both samples and, as hypothesized, acted as independent predictor of intention over and above TPB constructs when past behavior was controlled. This result is consistent with previous findings (Carfora et al., 2016; Caso et al., 2016; Rise et al., 2010). Individuals who perceived themselves as healthy eaters were more likely to develop the intention to consume three portions of fruits per day in the future. The unique effect of self-identity shows that the traditional antecedents of intention in the TPB, such as attitudes and subjective norm, do not account for self-related social influences on intention (Hagger & Chatzisarantis, 2006) and validate the inclusion of self-identity within the framework of the TPB 14
(Spark & Shepard, 1992). In addition, the examination of the relative effects of past behavior and self-identity demonstrated that, as expected, both self-identity and self-reported past behavior are important determinants of healthy intentions. Although self-identity might be inferred from past behavior (Fishbein & Ajzen, 2010), and thereby might act merely as a surrogate for the influence of behavioral habits, our results showed that the independent effect of self-identity persisted when past behavior is controlled. Consequently, this study further supports the proposal that the TPB should consider the role of self-identity in influencing behavioral intentions. However, Spark and Shepard (1992) and Spark (2000) identified several issues, especially related to the operationalization of the construct, which might instead measure attitude, past behavior or even intention. Recently, important suggestions about prototypical items to use for measuring self-identity over a wide range of behaviors and populations were provided by Snippe et al (2019). In conclusion, having a valid measure can be useful in order to decide if self-identity measures should be comprised or not in the socio-cognitive models like TPB. In the extended model, behavior was directly predicted by intention and past behavior, which had the strongest effects in both samples. The direct effect of past behavior on future behavior is coherent with our hypotheses and the results of previous studies (e.g. McEachan et al., 2011). However, contrary to our expectations, self-identity neither exerted direct nor indirect effects on behavior. This finding contrasts with the results of the meta-analysis by Rise et al. (2010), and the results of Caso et al. (2016), and Carfora et al. (2016). A reason for this inconsistency could be that self-identity measure refers to a general self-perception as a healthy eater, while the target behavior is very restricted. As argued by Dean et al. (2012), the discordancy between the two measures violates the principles of compatibility within the TPB framework (Ajzen, 1991), it may have had a role as regards our findings, and it should be carefully addressed in future studies. In both samples, PBC was the strongest predictor of intention, but did not directly influence behavior. However, in the literature, the overall evidence concerning the efficacy of PBC in directly 15
predicting behavior is not consistent (Godin et al., 2010), and our results are in line with previous studies on healthy eating (e.g. Carfora et al., 2016; Caso et al., 2016). Intention mediated the effects of PBC and subjective norm on behavior totally and the effect of past behavior only partially, thus indicating that consuming three servings of fruit per day is a behavior coming from both conscious deliberation and repeated and habitual behavior (for an extended discussion, see Hagger et al. 2018). Multi-sample analysis showed that the processes underlying intention formation and behavior execution are invariant between the two groups. However, some differences emerged in the mean scores, and, apart from subjective norm, students showed lower scores compared to adults. When projecting interventions to improve fruit consumption among young people, this evidence should be thoroughly considered. The current study has some inherent strengths: a) we differentiated affective and cognitive attitude, b) we analyzed self-identity effects when controlling for past behavior; c) we analyzed effects of TPB constructs, past behavior and self-identity on prospectively measured behavior; d) we examined the invariance of the processes for a specific behavior, in different groups and through multi-sample analyses; e) finally, to our knowledge few studies tested the TPB model for a specific healthy eating behavior – that is the consumption of three portions of fresh fruit –, and the current test significantly contribute to the body of evidence for the predictions specified in the model. Despite these points of strength, a number of potential limitations should be noted. We interviewed two convenience samples, with the student sample having a prevalence of women. Behavior was measured through self-report items, which could be subject to social desirability or social approval biases and retrieval inaccuracy. Nevertheless, this limitation is common in TPB-based studies on different eating behaviors and in those using more accurate self-reported measures such as a food diary (Miller, Abdel-Maksoud, Crane, Marcus, & Byers, 2008). A consequence of this limitation is that the effect of TPB constructs and other variables on objective behavior remains largely 16
unknown. Moreover, our study, although it considers a prospective measure of behavior, is correlational in design and does not allow the inference of causal relations.
4.1. Implications for Research and Practice From a theoretical point of view, our findings support the utility of decomposing attitude in affective and cognitive components, which show a differential relation with intention in the context of food choice. Such a finding suggests the importance of reinforcing the affective component of attitude, emphasizing the pleasantness and potential hedonic consequences of eating fruit in any intervention aimed to increase it. This point is also sustained by the fact that people may already be aware of the benefits of eating fruit, as shown by the high scores in the cognitive component of attitude, and a ceiling effect may have limited the predictive power of this construct (Blanchard et al., 2009a). PBC was the strongest predictor of intention in all models. To facilitate the perception of control, individuals must be supported to overcome obstacles and barriers (e.g., cost of fruit and difficulty in storage outside the home). Facilitating conditions (e.g., increasing the opportunities to buy ready-to-use fruit in cafeterias and vending machine) should be introduced to promote the development of intention to consume fruit. The research on this type of intervention is sparse. Nevertheless, a recent study on elementary classrooms showed that the presence of a refrigerator increases access to fresh fruit (Boehm, Schuknecht, & Cash, 2018). The importance of past behavior in predicting intention and behavior indicates that food consumption, although volitional and intentional, is almost certainly part of a consolidated food style built over time. Therefore, all educational programs aiming to consolidate fruit intake from a young age are very important. For our participants, a healthy eater identity was not predictive of the consumption of three servings of fresh fruit a day. This result suggests that the assessment of a general healthy eater self-identity may not be salient in consuming a specific amount of certain food groups (Brouwer & Mosack, 2015). In fact, for our participants, fruit consumption could not overlap completely with what it 17
means to eat healthy. For example, participants may have defined healthy eating in terms of calories restriction or avoiding fatty foods. In future research, measures designed to capture food-groupspecific identities (e.g., self-identity as a fruit eater) could improve the predictive ability of selfidentity with respect to the corresponding behavior (Carfora, Caso & Conner, 2017; Jung & Bice, 2019). In any case, more research is needed to understand the kind of consumption that defines a “healthy eating style”. Future research should also consider the possibility to assess other antecedents that may have an effect on healthy eating intention and behavior, such as anticipated affective reaction (e.g., regret for not having performed the behavior; Conner, 2018).
Acknowledgements The authors wish to thank Marta Da Dalt, Anna Sofia Fattor, Ilaria Inzaina, Libera Ylena Mastromatteo, Jacopo Noviello, Giulia Sovran, Dario Sperotto for their help in collecting and coding data for this research.
References Ajzen, I. (1991). The Theory of Planned Behavior: Some unresolved issues. Organizational Behaviour and Human Decision Processes, 50, 179-211. Armitage, C. J., & Conner, M. T. (1999). Distinguishing perceptions of control from self-efficacy: Predicting consumption of a low fat diet using the Theory of Planned Behavior. Journal of Applied Social Psychology, 29, 72-90. Bagozzi, R.P. (Ed.) (1994). Advanced Methods of Marketing Research. Oxford, UK: Blackwell Publisher. Bagozzi, R.P., & Edwards, J.R. (1998). A general approach for representing constructs in organizational research. Organizational Research Methods, 1, 45–87. Blanchard, C.M., Fisher, J., Sparling, P.B., Shanks, T.H., Nehl, E., Rhodes, R.E., Courneya, K.S., & Baker, F. (2009a). Understanding adherence to 5 servings of fruits and vegetables per day: a theory of planned behavior perspective. Journal of Nutrition Education and Behavior, 41, 3-10.
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Blanchard, C.M., Kupperman, J., Sparling, P.B., Nehl, E., Rhodes, R.E., Courneya, K.S., & Baker, F. (2009b). Do ethnicity and gender matter when using the theory of planned behavior to understand fruit and vegetable consumption? Appetite, 52, 15–20. Boehm, R., Schuknecht, T.J., & Cash, S.B. (2018). Cool Intentions: Adding Refrigerators to Elementary Classrooms Improves Fruit and Vegetable Access in a Pilot Study. Journal of Nutrition Education and Behavior, 50, 746-747. Brouwer, A.M., & Mosack, K.E. (2015). Expanding the theory of planned behavior to predict healthy eating behaviors. Nutrition & Food Science, 45, 39-53. Byrne, B.M. (1998). Structural Equation Modeling with LISREL, PRELIS and SIMPLIS: Basic Concepts, Applications and Programming. Mahwah, NJ: Lawrence Erlbaum Associates. Canova, L., & Manganelli, A.M. (2016). Fruit and vegetables consumption as snacks among young people. The role of descriptive norm and habit in the Theory of Planned Behavior. TPM: Testing, Psychometrics, Methodology in Applied Psychology, 23, 83-97. Carfora, V., Caso, D., & Conner, M. (2016). The role of self-identity in predicting fruit and vegetable intake. Appetite, 106, 23-29. Carfora, V., Caso, D., & Conner, M. (2017). Correlational study and randomised controlled trial for understanding and changing red meat consumption: The role of eating identities. Social Science & Medicine, 175, 244-252. Caso, D., Carfora, V., & Conner, M. (2016). Predicting intentions and consumption of fruit and vegetables in Italian adolescents. Effects of anticipated regret and self-identity. Psicologia sociale, 3, 319-326. Cohen J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. Conner, M. (2018). Experiential attitude and anticipated affect. In: Williams, D.M., Rhodes, R.E., Conner, M. (Eds.), Affective Determinants of Health-related Behavior. New York: Oxford University Press. Conner, M. T., Norman, P., & Bell, R. (2002). The Theory of Planned Behavior and healthy eating. Health Psychology, 21, 194-201. Conner, M., & Armitage, C. J. (1998). Extending the theory of planned behaviour: A review and avenues for further research. Journal of Applied Social Psychology, 28, 1430–1464. Conner, M., & McMillan, B. (1999). Interaction effects in the theory of planned behavior: Studying cannabis use. British Journal of Social Psychology, 38, 195-222.
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Dean, M., Raats, M.M., & Shepherd, R. (2012). The role of self-identity, past behaviour and the interaction in predicting intention to purchase fresh and processed organic food. Journal of Applied Social Psychology, 42, 669–688. Dunn, K.I., Mohr, P, Wilson, C.J., & Wittert G.A. (2011). Determinants of fast-food consumption. An application of the Theory of Planned Behaviour. Appetite, 57, 349–357. Eagly, A.H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich. Fishbein, M., & Ajzen, I. (2010). Predicting and Changing Behavior: The Reasoned Action Approach. New York: Psychology Press. Godin, G., Amireault, S., Bélanger-Gravel, A., Vohl, M. C., Pérusse L., & Guillaumie, L. (2010). Prediction of daily fruit and vegetable consumption among overweight and obese individuals. Appetite, 54, 480-484. Hagger, M.S., & Chatzisarantis, N.L.D. (2006). Self-identity and the theory of planned behaviour: Between- and within-participants analyses. British Journal of Social Psychology, 45, 731-757. Hagger, M.S., Polet, J., & Lintunen, T. (2018). The reasoned action approach applied to health behavior: Role of past behavior and test of some key moderators using metaanalytic structural equation modeling. Social Science & Medicine, 213, 85–94. Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. Istituto superiore di sanità. EpiCentro, Il portale dell'epidemiologia per la sanità pubblica. Centro nazionale per la prevenzione delle malattie e la promozione della salute [National Institute of Health. EpiCentro, the portal of epidemiology for public health. National Center for Disease Prevention and Health Promotion], (2019). http://www.epicentro.iss.it/passi/dati/frutta#dati. Accessed 15 February 2019. Jung, S.E., & Bice, C. (2019). The Role of Self-Identity in Predicting College Students' Intention to Consume Fruits and Vegetables. Journal of Nutrition Education and Behavior, 51, 173-181. McEachan, R.R.C., Conner, M., Taylor, N.J., & Lawton, R. (2011). Prospective prediction of health-related behaviors with the Theory of Planned Behavior: A meta-analysis. Health Psychology Review, 5, 97-144. McEachan, R.R.C., Taylor, N., Harrison, R., Lawton, R., Gardner, P., & Conner, M. (2016). Metaanalysis of the Reasoned Action Approach (RAA) to understanding health behaviors. Annals of Behavioral Medicine, 50, 592-612.
20
Miller T.M., Abdel-Maksoud, M.F., Crane, L.A., Marcus, A.C., & Byers, T.E. (2008). Effects of social approval bias on self-reported fruit and vegetable consumption: a randomized controlled trial. Nutrition Journal, 7:118. Reid, M., Sparks, P., & Jessop, D.C. (2018). The effect of self-identity alongside perceived importance within the theory of planned behaviour. European Journal of Social Psychology, 48, 883-889. Rhodes, R. E., Blanchard, C. M., & Matheson, D. H. (2006). A multicomponent model of the theoryof planned behaviour. British Journal of Health Psychology,11, 119–137. Ries, F., Hein, V., Pihu, M., & Armenta, J.M.S. (2012). Self-identity as a component of the theory of planned behaviour in predicting physical activity. European Physical Education Review, 18, 322–334. Rise, J., Sheeran, P., & Hukkelberg, S. (2010). The role of self-identity in the theory of planned behavior: A meta-analysis. Journal of Applied Social Psychology, 40, 1085–1105. Snippe, M. H. M., Peters, G. J., & Kok, G. (2019, January 23). The Operationalization of SelfIdentity in Reasoned Action Models: A Systematic Review of Self-Identity Operationalizations in Three Decades of Research. https://doi.org/10.31234/osf.io/ygpmw Sparks, P. (2000). Subjective expected utility-based attitude–behavior models: The utility of selfidentity. In D. J. Terry & M. A. Hogg (Eds.), Attitudes, behavior, and social context: The role of norms and group membership (pp. 31–46). Hillsdale, NJ: Lawrence Erlbaum. Sparks, P., & Shepherd, R. (1992). Self-identity and the theory of planned behavior—assessing the role of identification with green consumerism. Social Psychology Quarterly, 55, 388–399. Sparks, P., Shepherd, R., Wiering, N., & Zimmermanns, N. (1995). Perceived behavioural control, unrealistic optimism and dietary change: An exploratory study. Appetite, 24, 243-255. Stryker, S. (1987). Identity theory: Developments and extensions. In K. Yardley & T. Honess (Eds.), Self and identity, pp. 89-104. New York: Wiley. Stryker, S., & Burke, P.J. (2000). The past, present, and future of an identity theory. Social Psychology Quarterly, 63, 284-297. Terry, D.J., Hogg, M.A., & White, K.M. (1999).The theory of planned behaviour: Self-identity, social identity, and group norms. British Journal of Social Psychology, 38, 225–244. WHO (World Health Organization). Diet, nutrition and the prevention of chronic diseases. Report of a Joint/WHO/FAO Expert Consultation. (2003). https://www.who.int/dietphysicalactivity/publications/trs916/download/en/. Accessed 15 February 2019.
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Figure 1. Standardized path coefficients in the adult and student samples
Affective Attitude
Cognitive Attitude
Self-Identity .11* .13*
Intention
.28* .39*
Behavior
Subjective Norm
PBC
Past Behavior
Note: PBC: Perceived Behavioral Control. The first coefficients refer to the adult sample and the second ones to the student sample. * p < .05
Predicting Fruit Consumption: A multi-group application of the Theory of Planned Behavior
Highlights -
A TPB-based two-wave multi-group study is performed.
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The TPB framework is extended with measures of past behavior and self-identity.
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Both the affective and the evaluative component of attitude are assessed.
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Past behavior but not self-identity shows a significant link with future behavior.
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The relation model fits the data well and proves to be invariant across groups.