Abstract
The article concentrates on the delineation between children’s participation and children’s influence in a family purchasing process. Lack of clarity between the two concepts results in misconceptions, inconsistencies, or even conflicting findings across studies. This study addresses the issue from theoretical and methodological perspectives. Taking into account the importance of children’s participation as a necessary, but not sufficient, pre-condition to demonstrate the influence, the study delineates the two variables and specifies the differences between them. This is supported with the development and validation of an alternative scale that directly measures children’s influence. Further analysis allows justification of the new scale and shows a theoretically supported difference between the measurements of children’s participation and influence in family-buying decision. The fact of making clear distinction between participation and influence leads to the enriched theoretical and methodological knowledge in the field and provides important managerial implications both in the family purchasing context and in other types of group interactions.
The field of research on children’s influence on parental purchase decisions enjoys substantial empirical evidence obtained from different countries over decades, including studies related to various products, types of families, and demographics of children and their parents. Systematic reviews of studies on children influence (Dikcius et al., 2016; Mangleburg, 1990) demonstrate that only part of the outcomes attained by the studies is more or less consistent in their findings: They find that children’s influence is higher in the cases where a product is purchased for a child’s personal use, the products are inexpensive, or the children are relatively older. Notwithstanding the use of similar variables, other findings are varying and sometimes controversial.
Although part of the variety of research outputs may be attributed to different aims of studies, variety of contexts and a diversity of factors considered, at least some of them may be linked with the methodological questions regarding the key dependent variable: children’s influence in family purchase decision. It is noteworthy that children’s influence is usually measured with various scales and scale items and, therefore, reflects not the same type and strength of the role that children play in parental purchases (Dikcius, Pikturniene, & Reardon, 2017; Mangleburg, 1990). The most apparent difference between the measurements is related to focusing on either children’s participation in the purchasing process/its various stages or the influence on purchase outcome, that is, on a final decision. The analysis of numerous studies shows that the concepts of children’s participation and influence are sometimes used as synonymous and lack clear delineation (Dikcius et al., 2017). At the same time, the conceptual clarity in scale application is critical (DeVellis, 2016), as seemingly similar domains differ in both behavioral and outcome characteristics.
According to a classical approach, a purchase decision–making process in a family entails the sequential advancing toward the output with the assumed interpersonal influences (Schiffman & Kanuk, 2007). The participants of the process play specific roles and functions within the group (family) and have different levels of influence on purchase decisions; sometimes, their influence varies across the stages. The analysis by distinctive stages makes the issue even more complicated: Active participation of children in one of the stages is not necessarily equally strong in others. In addition, participation in particular actions or functions in the individual stages of the process means not the same as the influence on the final decision: Parents’ final decision can be affected by a child on the very last moment by shaping the intended price, model, place, and so on, without his or her participation in the early stages of the purchasing process. Although there are well-acknowledged operationalizations for some types of children’s participation or influence on parental purchase decision (Beatty & Talpade, 1994; C. Kim & Lee, 1997; Talpade & Trilokekar-Talpade, 1995), the influence of children on the final outcome of the parental purchase decision scale is offered not systematically, often by proposing ad hoc modifications of scales that have never been validated using the established procedures.
This study aims to develop and test a scale aimed to measure children’s influence on family purchasing outcome and provide the empirical evidence on how this type of influence is related to or differs from the scale that mainly concentrates on assessing the children’s participation.
To achieve this, a broader concept of children’s engagement was categorized into the following components: children’s individual decisions, children’s participation in the decision-making process, and children’s influence on a family decision outcome. The empirical analysis provides validation of the direct children’s influence on the parental purchase decision scale, and demonstrates its distinctiveness and relationship with the previously used children’s participation scale.
The study includes two surveys: one focuses on the children and their parents, while the other on parents only. The first survey allowed to collect the data about the children’s direct influence on the final parents’ decision to purchase 14 products, as perceived by parents and children. The data obtained from this survey allowed verifying children’s influence on the parental purchase decision scale. The second survey was focusing on the data obtained from parents only; it was used to test the validity of children’s influence scale and the relationships and differences between the domains (criterion validity).
The issue of clear distinction between participation in a group process versus the influence on the final group decision is very important not just for scientific but also for managerial purposes, and it goes well beyond the context of family buying. Direct assessment of the influence of the specific group participants is needed for efficient management of group processes and for development of the insights in planning marketing and communications. In all these instances, the segregation between one’s participation in a process and influence on the process outcome is very important.
Theoretical background
Family purchase decision and the roles of participants
Consumer behavior with a special attention to purchase decision making has been of major interest to marketers for many years (Madhavan & Kaliyaperumal, 2015). As many approaches and models for the decision-making process have been used in studies (Bray, 2008), they evolved into the typologies of decision-making models that are linked with the theories grounding the model types (Abelson & Levi, 1985; Foxall, 1990). Among the theories that explain purchase decision–making processes, the information-processing theory takes almost the central place (Bettman, Luce, & Payne, 1998; Gabbott & Hogg, 1994). On the basis of this theory, Nicosia (1966); Engel, Kollat, and Blackwell (1968); Howard and Sheth (1969); and Gilbert (1991) have developed the earliest and the most influential models—“grand models” of consumer behavior. These models present the purchase decision–making process as a logical flow of activities that sequentially flow from the problem recognition to purchase and to the post-purchase evaluation stage. Typically, in the case of rational decision making, the purchase decision–making process is described as a sequence of five main stages: (a) problem recognition, (b) information search, (c) alternative evaluation and selection, (d) purchase decision, and (e) post-purchase evaluation (Erasmus, Boshoff, & Rousseau, 2001; Schiffman & Kanuk, 2007). Although this traditional five-step approach is sometimes criticized for not taking into consideration all the possible instances (not all the steps are involved in every purchase, some types of buying are shorter and almost instant, like impulse buying), the fact that the process is affected by other more complex influences (Arutselvi, 2012), it well outlines the grounding idea of the purchasing process flow. Therefore, it is widely used in cases where the analysis of the sequential activities is important (Erasmus et al., 2001; Moital, 2006), including the cases assessing the role of children in the family-buying process (Norgaard, Bruns, Christensen, & Mikkelsen, 2007).
The key element of grand models is the consideration of this process from the standpoint of an individual who searches, evaluates, and stores information Gilbert (1991). The theory of family-buying decisions has extended this view by categorizing the decisions made within a family into two types: autonomous, that is, made by a single member of a family, or joint—performed by several or all members of the family (Sheth, 1974). It evolved into understanding the most typical instances in a family purchase decision–making process: (a) husband-dominated decisions, (b) wife-dominated decisions, (c) autonomous decisions (either husband or wife is the primary or the sole decision maker, but not both of them together), and (d) syncratic or joint decisions, where both husband and wife are influential (Herbst, 1952). Since then, substantial evolution of the family structure and, subsequently, family concept has happened: single-parent families, same gender families with adopted or one spouse’s children, non-married parents, and, increasingly, repetitive marriages, raising children from previous families jointly, became common in society (Dinisman, Andresen, Montserrat, Strózik, & Strózik, 2017; Hadfield, Amos, Unga, Gosselin, & Ganong, 2018; Marceux, 2019). Thus, the notion of “family” is also often applied for structures that differ from a traditional family composed of husband, wife, and their mutual children (Dinisman et al., 2017). Although there are studies that demonstrate minor financial and purchase behavior–related differences in non-traditional and traditional families (Ahuja & Walker, 1994; Dinisman et al., 2017; Razzouk, Seitz, & Prodigalidad Calpo, 2007), the current study is integrating them together.
When purchasing decisions are made by more members of a family (including children, grandparents), the “buying centre” approach is helpful for describing the roles of family members. As a minimum, a buying center includes five roles, as identified by G. E. Belch, Belch, and Ceresino (1985): initiator, influencer, decider, buyer, and user. It is understood that an initiator is the family member who proposes an idea regarding a purchase. An influencer establishes the decision-making criteria (typically—on the basis of comparisons) and tries to persuade other family members during the process. A decision maker has the power to unilaterally or jointly decide regarding the decision to purchase or not a specific brand, product, or service. A buyer carries out the decision by technically performing the act of purchasing. A final user is the one who uses the product and evaluates it, giving some feedback to other family members regarding the satisfaction with the chosen brand and desirability to purchase the same brand or product again. Several roles may be undertaken by the same family member, but in many instances, different family members have different roles for specific products, or they influence the purchase decision–making process in a different way (Chisnall, 1985; Mowen, 1987).
To some extent, the roles may be linked with the specific stages of the purchase decision–making process, where their impact is the largest (Jensen, 1990). Obviously, initiators play their role just in realizing a problem, influencers may act throughout all the process, the final decision making is mainly linked with decision makers and buyers, while users play the most important role in post-purchase decisions (Verma & Kapoor, 2003). This means that a decision maker may experience various influences from other participants, but the act of the decision is typically made by one person who plays the role of a decision maker either by combining it with other roles or not. Therefore, the studies that concentrate on the outcome of the purchasing process may hardly consider a family-buying process from the perspective of joint decision making; it is rather a unilateral decision with the possibility of various influences.
Children’s participation in and influence on parental purchase decision
The role of children in family purchases might range from cases where a child does not participate at all to fully autonomous decisions of a child; however, in many cases, a child’s role means only his or her participation in certain stages of the process or their ability to influence parents’ decision (Dikcius et al., 2017). Both of the extremes (decision made by either only parents or only by a child) mean entirely autonomous decisions that exclude the participation and influence of other family members, only partially belongs to the research domain that concentrates on family decision making. The analysis in family decision making mainly focuses on purchasing processes where children interact with other family members; this approach is also employed in the current study.
Children are naturally involved in everyday family routines through daily interactions, top-down instructions, and repetition as part of their development (Rogoff et al., 2007) or as part of consumer socialization (Foxman, Tansuhaj, & Ekstrom, 1989). The nature of family communication implies that children are often around when various decisions are made. Therefore, it is natural that parents share information with them regarding product needs, purchasing options, locations, prices, and so on. If children assist parents in a store, again, they might serve as information collectors and price checkers, can pick products for family purchases on a routine basis, and can address alternative options. In other words, children may more or less actively participate in a family purchase process.
Anaby and Law (2013) claim that there is no universal agreement on the definition of the participation term due to a variety of participation forms, participation activities, and areas where participation takes place. Imms et al. (2016) have performed a systematic literature review and conclude that participation consists of two elements: attendance and involvement, with attendance being a necessary prerequisite to involvement. It goes in line with the common English definitions of participation: “to take part in or become involved in an activity,” “sharing in common with others,” and “receiving or having part of something” (Simpson & Weiner, 2002). Participation in a purchase decision–making process, first of all, means the presence of a person within the process. If considering the stage-based decision-making approach, children in a family purchase decision may take part in various stages of this process by playing roles of problem recognizers, interested parties, experts, information collectors, and purchasers (Norgaard et al., 2007).
The level of participation might vary from passive to very active, and this is categorized by relevant typologies. Arnstein’s (1969) “Ladder of Participation” presents sequential degrees of citizen empowerment in community planning and decision-making activities through eight levels of activities. This concept has served as the basis for the development of child-related classifications of the participation (Boyden & Ennew, 1997; Hart, 1997; Treseder & Smith, 1997; Vis & Thomas, 2009). The minimal level of participation is limited to the presence of a child in the family’s purchase decision—a child could be informed about parents’ intention to buy a product. Even though this type of participation is very passive, it is a necessary prerequisite for more active forms of participation when a child performs certain tasks/actions. For instance, after informing a child, parents may be asked questions or hear their wishes. In other cases, the whole process could be triggered by a child’s initiation. Children can (or may be asked to) collect information about products, brands, prices, places, or even the best time for purchasing, and present alternative solutions for their parents. Children may even negotiate with parents on various aspects of the product, place, and time of purchase, which means a higher level of their participation.
However, all of the above-mentioned forms of children participation do not necessarily mean the influence on the parental purchase decision, as the influence implies the transformation of parental decision toward the demands or preferences of children. By definition, the influence of an actor on partner occurs when a partner changes his or her decision, thoughts, behavior, or emotions (Baumeister & Vohs, 2007; French & Raven, 1959; Huston, 2002). Opposite to participation, children’s influence on a purchase decision would be accountable when parents do change their decision, compared with their decision that would have been made without their child’s impact or ignoring their children’s wishes, desires, or opinions (French et al., 1959). It is important to state that a higher level of participation does not necessarily mean the same increase in the influence: Child participation might be active but not productive, that is, it may not influence the final decision as a decision maker might not consider the child’s input. The influence is also hardly visible in cases where interests/opinions of a child are very similar to those of other family members, especially when they are in line with the decision maker’s preferences. In this case, the participation of a child is reflected as a contribution to the decision just in the form of confirmation, acceptance, or compliance with the decision. All this allows to develop the hypothesis that predicts uneven overall levels of children’s participation and influence:
H1. Children’s participation in a purchasing process is higher than their influence on parental purchase decision.
However, child participation in the initial stages of a purchasing process might have a different background on the process outcome than that in the later stages (information search, decision making). When a child initiates a process, parents naturally respond to the child’s need as predicted by the in-group bias concept (Brewer, 1979)—parents (typical decision makers) are willing to consider the interests of their children expressing their needs. This claim is even stronger supported when the child–parent relationship is viewed through the lens of the theory of social interdependence (Johnson & Johnson, 2005). It could be argued that ultimately children and parents have the same goal of inter-family harmony, happiness, full-integration of family members, and other aspects of qualitative relationships. Thus, the purchase process aims to achieve the ultimate goal of family well-being via trust and compromise to reach the outcome that satisfies all the parties. Even if a common attitude toward the idea of the purchase or its aspects is initially not shared with a child, parents tend to balance their attitudes to avoid dissonance (Heider, 1958). Therefore, the hypothesis is
H2. Children’s influence on parental purchase decision increases as children’s participation in the need recognition stage grows.
In line with the increasing age of children, their influence becomes stronger—at least for certain products and Internet-related issues (M. A. Belch, Krentler, & Willis-Flurry, 2005; El Aoud & Neeley, 2008). This can be interpreted as an effect of reverse socialization (Roedder, 1999) or a growing informational and expert power of a child based on the available resources of time, knowledge, and, sometimes, money (Flurry, 2007). A stronger social and expert power gained by children enables them to be more efficient in the later stages of the process: information search and decision making. More active participation in these stages predicts a stronger influence on the output of the process:
H3. Children’s influence on parental purchase decision increases as children’s participation in the stages of information search/decision grows.
To test the above-formulated hypotheses, well-segregated child participation and child influence measures need to be conceptualized and developed.
Measurement of children’s participation and influence
Researchers have used numerous scales and items/questions for measuring children’s participation or their influence in parental purchase decisions. Dikcius et al. (2017) identified more than 80 research instruments in use; however, the majority of the scales have been developed ad hoc and tested deploying only relatively simple procedures of reliability tests (exploratory factor analysis [EFA], Cronbach’s alpha) or even measured participation/influence with only one item. The range of used measures starts from the direct statements evaluated on a Likert-type scale or listed options, for example, “When my child uses this (pestering technique), I tend to yield (purchase)” (Shoham & Dalakas, 2006) or “Teen played a role in your decision to get him or her vaccinated or not to get him or her (vaccinated)” (Dorell, Yankey, Kennedy, & Stokley, 2013). However, several measurement scales that assess children’s participation in or influence on parental purchase decisions have gained a considerably wider recognition.
One type of measures of children’s influence on parental purchase decision is based on asking about the child’s influence in regard to purchasing numerous individual products. Then, the answers are aggregated by product groups, and the measure of the influence is applied for the whole product group (Dikcius, Armenakyan, Urbonavicius, Jonyniene, & Gineikiene, 2014; Isin & Alkibay, 2011; Laroche, Yang, Kim, & Richard, 2007; Sondhi & Basu, 2014). The products may be selected randomly or following a certain classifying logic. The robustness of this measure is based on the consistency of products within a group. If they are similar within a group and different across the groups, the measure holds. Therefore, the measure demonstrates good validity and reliability in cases where product categories are logically sound (Dikcius et al., 2017).
Beatty and Talpade (1994) have validated perhaps the most acknowledged scale called “a relative adolescent influence scale.” It captures the purchase initiation stage with four statements signaling the initiator’s role, and the information search/decision stage with five statements that assess the functions/roles of an adolescent in examining brands or models or picking products at a store. The response formats of this scale ask to indicate who contributed to the particular function or role in a given stage, where 0 means “I did not contribute at all” and 6 “the entire contribution was mine.” The scale has been used or adapted in numerous studies (M. A. Belch et al., 2005; El Aoud & Neeley, 2008; Fikry & Jamil, 2010; Flurry, 2007; Liang, 2013).
Majority of researchers who use this scale assume measuring the influence of a child in family buying. This is appropriate, if a study does not aim at emphasizing the differences between children’s participation and their influence. If these two aspects are differentiated, it becomes obvious that the scale allows to measure children’s participation in different stages of the process rather than their influence. As the scale has never been tested as a second-order variable, there is no possibility to confirm that it measures the overall influence on parental decision. The categories of answers in the scale indicate a measuring contribution of a child and a parent at separate stages, and therefore, many authors have used the terms “contribution” and “participation” as synonyms (Cabrera & Cabrera, 2002; Collis & Strijker, 2004; Nguyen et al., 2015; West, Guthrie, Dawson, Borrill, & Carter, 2006; Zhang & Peck, 2003). However, other researchers formulate that the contribution is a measurement of the strength of participation (Kullenberg & Kasperowski, 2016; Kuppelwieser & Finsterwalder, 2011). The contribution does not necessary trigger the change in the purchase decision in terms of its happening or in terms of its aspects (sub-decisions). Therefore, it seems that the scale developed by Beatty and Talpade (1994) is measuring children’s participation and perhaps its strength (contribution), but is less applicable for the measurement of the influence that children exert on parental purchase decision—the final output result of the purchasing process.
Another approach of measuring children’s influence ignores the purchasing stages and concentrates only on the process output—the final decision. Having this in mind, this approach is consistent with the definition of influence (French et al., 1959), as it tracks to what extent children made the difference in opinion or behavior of parents. As the decision includes numerous aspects, they can be measured separately as sub-decisions. If children’s influence on each sub-decision may be assessed, the aggregation of these sub-decisions would mean the level of children’s influence on purchase decision. Following this logic, numerous studies measure the strength of influence on individual aspects of purchase decision defined by questions “What?” “When?” “How much?” and “Where?” This approach has been used to measure children’s influence and was considered to be applicable for use in the field (Caruana & Vassallo, 2003; Mangleburg, 1990). However, the scale items have varied between studies, by adding or removing aspects/sub-decisions and mixing them with statements about children’s roles or actions during the purchasing process. In some studies, the sub-decisions have not been aggregated, and the children’s influence on product purchase has been reported as mean comparisons among the influence for different aspects of the decision, or among the various products (Shoham & Dalakas, 2006). In some cases, scholars track on what element/sub-decision a child makes the most significant influence, but do not consider the influence as the aggregation of sub-decisions, as a composite measure of influence (G. E. Belch et al., 1985; Darley & Lim, 1986). This approach (measuring the influence on the basis of sub-decisions) seems to be promising if adequately elaborated and tested. This outlines the necessity to develop an alternative scale that would measure children’s influence on the basis of sub-decisions, and to compare the measurement results with those achieved with a scale that concentrates on child participation.
To extend prior studies and develop a scale of children’s influence on parental purchase decisions (later—children’s influence), we have followed Churchill’s (1979) scale-development paradigm to assure the process of valid and reliable scale development. The process involves three phases: scale generation and purification, scale refinement, and scale validation.
Scale development methodology and results
Scale generation and purification
Face validity of the scale was assured by collecting the initial spectrum of parental purchase sub-decisions influenced by children or made with their participation that were deployed by other scholars. According to the theory of purchase decision making, children’s effect on family decisions is decomposed into the most common elements children might exhibit their influence on the final decision. Kotler and Keller (2012) explain that during this stage, consumers make decisions, attempting to answer five questions: Which product (brand), Where (dealer), How much (quantity), When (timing), and How to pay (payment method). Other researchers agree with the concept, but use various number of sub-decisions. Darley and Lim (1986), as well as Karmakar (2016), have analyzed only three sub-decisions: When to buy? How much to spend? and Where to buy? Chaudhary (2015) uses the same questions and adds one more question: Which to buy? Warayuanti and Suyanto (2015) have split the last one into two sub-decisions—one related to brand choice and the other to product choice. Kancheva (2017) and Martensen and Gronholdt (2008) include a list of criteria related to a product (for instance—real estate). G. E. Belch et al. (1985) use different sub-decisions depending on a product. The decisions related to color and product brand have been analyzed for most of the goods. Among others, the most popular are sub-decisions, related to the amount of money, place, and time of purchase. However, specific, product design–related decisions have been included (model and style) in selecting an automobile, television, household appliances, and furniture, while the size and type is considered in the case of breakfast cereal. The same sub-decisions (When to buy, How much to spend, Brand, Model, Color, Where to buy) have been used by Kaur and Medury (2011) in research of technological products. Wang, Hsieh, Yeh, and Tsai (2004) add more choice to a range of sub-decisions in terms of products/services: airlines, restaurants, coaches, shopping, optional tours, travel agencies, and tour leaders for selection of travel destinations. Arutselvi (2012) extends a list of sub-decisions, up to 10, for purchasing of durable goods (an idea of purchase, how much to spend, choosing from brands, features, styles, types, sizes, stores, time for buying, and modes of payment). Furthermore, Liao et al. (2017) and S. S. Kim, Choi, Agrusa, Wang, and Kim (2010) analyze even 14 sub-decisions related to purchasing travel services.
Previous studies have shown that researchers use various sub-decisions related to the purchase. The most typical sub-decisions as the need recognition, place for purchase, time for purchase, price of product, brand, and specific characteristics of a product were related to the measurement of the influence of family members on decision making when buying rather different products (Akinyele, 2010; Ashraf & Khan, 2016; Guneri, Yurt, Kaplan, & Delen, 2009; Patel & Bhatt, 2014; Siraj, 2013). Extensive analysis of the former studies allowed to find the key aspects that are typical for the majority of instances and to aggregate them into a newly suggested scale of Children’s Influence on Parental Purchasing Decision. The scale includes six statements: “A child changes your previous opinion/decision on (a) the features of the product (product functions, design, and technical characteristics), (b) the brand of the product, (c) the price of the product, (d) the place of purchase, (e) the time of purchase, and (f) the need for the product.” The respondents were asked to show the degree of their agreement with the statement on each product on a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree). The formulation of the general statement “A child changes your previous opinion/decision” is deliberately missing the term “influence” to avoid direct connotations. Instead, it is formulated in a manner to reflect the classical definition of the influence, “Actor A changes opinion/behavior of Actor B,” given by French et al. (1959).
Children’s influence scale refinement and confirmation—Study 1
The children’s influence scale was refined on the basis of the responses obtained from a cross-sectional survey performed in Lithuania by a research company using the Internet panel. The respondents were families consisting of parents with at least one child from 12 to 18 years of age. This age group was selected in an attempt to avoid an additional impact of children’s age on the results. Both children and their parents were instructed to fill out the questionnaires independently. The wording of the major statement was adjusted correspondingly to the respondent: children reported their influence and parents reported their children’s. Parents, when responding, were asked to refer to the eldest child of the family within the given age range. In total, 912 respondents (304 families with three participating members) were surveyed.
Initially, seven product categories were proposed (see Table 1). As previous studies have demonstrated that children’s engagement (influence, participation) into parental purchase decision is dependent on the final user (Dikcius et al., 2014; C. Kim & Lee, 1997; Mangleburg, 1990), the current study loads two products on each category: one of the products more addressed to children’s use and the other more suitable for the use of the whole family/adults. Such a selection of products allows the evaluation of the children’s influence scale reliability for different product groups and users.
Groups of products on which the children’s influence scale was tested.
FMCG: fast-moving consumer goods.
To avoid a lengthy instrument and to get reliable results, a fractioned factorial design was applied by the final consumer of the product. Sample A (153 families) evaluated three products for the whole family’s use and four products for children’s individual use, while the respondents included in Sample B (151 families) answered the questions on four products for the whole family’s use and three products for children’s individual use. There were no differences in the gender, age, education, parental income, and children’s age of respondents included in both the samples (see Table 2).
The demographic data of respondents in both samples.
An EFA was undertaken to identify a priori dimensionality of the children’s influence scale for each product. The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity were computed to assess the appropriateness of factor analysis to the data. The KMO value ranged from 0.80 (for a bicycle) to 0.93 (for a coffee table) (see Table 3). Bartlett’s test was significant at the .001 level for all cases. Both the results indicated sampling adequacy for the analysis (Hair, 2010).
Results from EFA.
EFA: exploratory factor analysis; KMO: Kaiser–Meyer–Olkin test.
The factor scores were generated with six items by two criteria: (a) eigenvalues greater than one and (b) factor loadings greater than 0.50 (Hair, 2010). Only one-factor solution was received in each case for 14 products on the basis of eigenvalues. One-factor solution generated a satisfactory level of cumulative percentage which was higher than 50% in all cases (except for a bicycle). Factor loadings were higher than 0.5 in all products and typically varied between 0.7 and 0.85. Finally, the scale reliability was good—the Cronbach’s alphas were between .8 and .9 in most of the cases, which is more than the required .70 level (Nunnally, 1978).
Children’s influence scale validation—Study 2
Data collection
As it was necessary to track more profound patterns of child–parent interrelationships, two high involvement products were selected: a mobile phone for the child’s individual use, and a TV set for the whole family’s use. As in the previous survey, there was a deliberate delineation between children’s personal products and family products, which could be expected as the influence patterns in purchase decisions differ depending on the final user.
The data for the validation of children’s influence scale were collected from the Internet surveys performed by research companies in Lithuania and the United States. The parallel study in the United States allowed to test the English version of the new scale that initially has been developed in Lithuanian, and to ensure that the scale is not country/culture specific. In both countries, the respondents were parents with children aged from 12 to 18 years. If a respondent had several children aged within the appropriate range, they had to provide answers about the eldest child. A total of 561 respondents completed questionnaires (403 in Lithuania and 158 in the United States). The distribution of the questionnaires according to the gender of children was similar (48% of girls, 52% of boys), with mean age of 15 years. The respondents’ proportion by gender was 47% of men and 53% of women, and their average age was 42.8 years.
The US sample was contributory in testing the English version of the children’s influence scale. The questionnaire items were translated by six independent bilingual individuals (three native Lithuanian and three native English speakers). The translated version of the questionnaire was compared for any inconsistencies, mistranslations, and lost meanings or words by the authors.
Measures
As in Study 1, children’s influence scale of the six items was used to measure children’s influence. It was repeated twice for two products. Also, the scale developed by Beatty and Talpade (1994) was included to measure children’s participation in parental purchase decision. The measure addresses children’s participation in two decision-making stages: need initiation and information search/decision making. A 7-point Likert-type scale was used, with the original response formats: “My son or daughter did not contribute at all vs Entire contribution was my son’s or daughter’s.”
Scale reliability tests
Reliability of children’s influence and participation scales was tested by Cronbach’s alphas for internal consistency. As shown in Table 4, the children’s influence scale values for Cronbach’s alpha were .89 for mobile phone and .93 for TV. The participation scale values for Cronbach’s alpha in need initiation and information search/decision scales were between .90 and .91 for a mobile phone and .95 and .96 for a TV. All the scales possess good internal reliability.
Cronbach’s alpha coefficients denoting internal consistency for both countries.
Confirmatory factor analysis
To confirm the structure of children’s influence scale for the samples from both the countries, a confirmatory factor analysis (CFA) with Mplus 5.0 (Muthén & Muthén, 2006) was conducted. CFA models were evaluated using three goodness-of-fit indices: CFI (comparative fit index; Bentler, 1990), RMSEA (root mean square error of approximation; Browne & Cudeck, 1993), and TLI (Tucker–Lewis index; Tucker & Lewis, 1973). We tested two models—influence in purchasing a mobile phone and influence in purchasing a TV. In both models, several correlated errors (between first and second, second and third, fourth and fifth, fifth and sixth, and first and sixth questions) were added. In children’s influence scale models with six measurement variables and one latent variable showed acceptable goodness of fit—χ2(4) = 10.60, p = .03; CFI = 1.00; TLI = 0.99; RMSEA = 0.05; χ2(4) = 5.14, p = .27; CFI = 1.00; TLI = 1.00; RMSEA = 0.02—for a mobile phone and a TV, respectively. Factor loadings of the models are presented in Table 5. Goodness of fit criteria and factor loadings from these models suggest that the structure of CI scale is quite strong; therefore, it seems that one-factor structure is the best option for this scale.
Factor loadings for children’s influence scale.
The first number is for a mobile phone model, and the second is for a TV model.
Criterion validity: the relationship between participation and influence domains
To evaluate the relations between the domains of children’s influence scale and the children’s participation scale domain, we used the structural regression model (model presented in Figure 1). This model was used to test whether children’s participation domains (for need initiation and information search/decision making) could predict children’s influence. All these links observed in the tested models were estimated simultaneously. Two different models were used: for the purchase of mobile phone and for a purchase of a TV set.
Impact of children’s participation on their influence on parental purchase decision.
Both the models showed acceptable goodness of fit—χ2(81) = 314.39, p < .001; CFI = 0.96; TLI = 0.95; RMSEA = 0.07; χ2(80) = 278.44, p < .001; CFI = 0.98; TLI = 0.97; RMSEA = 0.07—for mobile phone and TV, respectively. Both the models suggest that there are no significant relations between participation in need initiation stage and influence (Est = –0.01, Est = 0.02, for a mobile phone and a TV, respectively). Such results enabled us to reject H2 (children’s influence on parental purchase decision increases as children’s participation in the need recognition stage grows). However, in both models, participation in information search/decision stage has an impact on children’s influence (Est = 0.44***, Est = 0.54***, for a mobile phone and for a TV, respectively), which proves H3 (children’s influence on parental purchase decision increases as children’s participation in the stages of information search/decision grows). Thus, the results suggest that participation in need initiation has an indirect impact on the influence, while participation in the decision-making stage turns into the influence on the final decision when the outcome of children’s interaction is evaluated.
Comparison of children’s influence and participation domains in parental purchase decision
To evaluate the mean values of used scales, first paired-sample Student t test was performed. We compared children’s influence while purchasing a mobile phone (a product for child’s use) and purchasing a TV (a family use product). The results suggest (see Table 6) that there are significant mean value differences between purchasing a mobile phone and a TV (t = 13.72, p < .001) (M = 4.12, SE = 1.56 and M = 3.09, SE = 1.68, respectively). Thus, children’s influence is stronger in purchasing a mobile phone. Significant differences in children’s participation in information search were revealed by comparison of the mean values of children’s participation in purchasing a mobile phone or a TV (t = 18.96, p < .001) (M = 4.99, SE = 1.45 and M = 3.61, SE = 1.83, respectively) and in participation in information search/decision making (t = 16.41, p < .001) (M = 4.36, SE = 1.64 and M = 3.27, SE = 1.83, respectively). These results suggest that children’s participation is much stronger in purchasing products for their own use rather than for the use of the whole family.
Mean differences of children participation (two stages) and children influence (for two products).
ANOVA: analysis of variance.
The first number is for a mobile phone model, and the second is for a TV model.
The comparison of mean values with repeated-measures analysis of variance (ANOVA) (three different conditions) of children’s participation in purchasing a mobile phone shows significant mean differences (Wilks’ lambda = 0.74, p < .001). Bonferroni post hoc test has indicated that all three mean values are significantly different. Thus, children’s influence is perceived as much lower compared with both types of participation when the decision to buy a mobile phone is made. Similar results were obtained from the data on purchasing a TV (Wilks’ lambda = 0.90, p < .001). Bonferroni post hoc test has repeatedly shown that all the three mean values are significantly different, with the highest value skewed toward participation in need initiation stage. These results support H1 (children’s participation in a purchasing process is higher than their influence on parental purchase decision).
Discussion and conclusion
This study concentrates on the elaboration of the theoretical and methodological distinction between the concepts of children’s participation and children’s influence in family-buying process. As a result, a new scale that directly follows the concept of the influence has been developed and tested; the findings have been compared with the results shown by one of the most popular scales in the field. Although participation is important from the point of view of researchers and marketers, influence, which means effecting a change in parents’ decision making, is clearly much more so. Thus, the very fact of making this distinction is useful for the community.
The findings allow to admit that probably the most acknowledged relative adolescent influence scale (Beatty & Talpade, 1994), typically used for the assessment of children’s influence, in fact, reports a combination of children’s participation and their influence, with the main focus on the first. A newly developed scale measures only the efficient influence of children in terms of changing the sub-decisions made at the final purchasing process stage and presents comparable but different results.
More specifically, the comparison of children’s participation and influence exhibited during the two stages of purchasing process allows to confirm the assumption that children’s participation at the initial stage of the purchasing process is not significantly related to the influence. The participation in the final stages of the process has a significant relation with the influence; however, the measure of the influence with the use of the newly developed scale (on the basis of sub-decisions) is stricter and allows to ignore inefficient participation that does not generate the change in the final parental decision. This has several theoretical and practical implications.
There are instances when the element of participation is of key importance. For example, it might be needed in assessing the differences of children’s participation between families of different types/composition, parenting styles, when studies concentrate on cross-cultural issues, and so on. In these cases, we suggest using the known scales to assess the needed variable either for the certain stages of the process or the whole process. We could only suggest using the term “participation” instead of influence, as the latter is partially misleading.
However, in many cases, researchers or practitioners need to assess the specific variable of an influence that would measure the strength of the influence in terms of the degree to which the decision has been modified by an influencer. This study concentrates on the influence of a child, but the suggested concept of the measurement (by sub-decisions) can have a much broader application.
First, it may be considered for other numerous group decision–making processes such as the ones that occur in organizations. This would allow to measure the efficiency/importance of the influence exerted by each member of the modifications/adjustments of group decisions. Specifically, this could be important in assessing the influence of outside experts/consultants on the group decisions, when they are hired to assist in complex group projects. This is at the very core of managerial interests, as hiring outside experts has to be well justified.
Second, the new influence measure may be well linked to the assessment of one’s participation in the process, as participation is a necessary, but not sufficient, pre-condition for the influence. As for group opinion consistency and confirmatory participation, the measure of influence would hardly be correlated with the participation, or the correlation would be weak. However, in cases of diverse initial opinions of group members, the assessment of the link between the participation and influence may be helpful to develop measures of efficiency of individual performance within a process, which is important in numerous managerial practices.
Third, the observed differences in the relation between participation and influence in the starting and final stages of the process suggest that only the participation in the final part of a group process can be directly linked with the measure of influence. Participation in the early stages may be important, but they are mediated by the participation in the final stages. This is an important finding for the analysis of family purchasing, but again, it may be considered at least to be tested in other types of group behaviors and processes.
From the managerial standpoint, the distinction between participation and influence allows development of more precisely targeted marketing/communication techniques that aim to address group interactions. It is obvious that targeting the points of high influence is much more productive than focusing on those of intense participation, but low or unknown influence.
All this together justifies the novelty of the findings from the theoretical, methodological, and managerial perspectives and suggests further directions for research.
Limitations and future research
The current study has several limitations demanding further clarification with future research. Some authors (Batounis-Ronner, Hunt, & Mallalieu, 2007; Chavda, Haley, & Dunn, 2005) point out that children’s influence on parental decision could be product-dependent. Numerous studies concentrated on various child- and family-related products, but most of them are tangible goods, not allowing the certainty that the same happens in the case of services. Considering the specificity of services could be the next challenge for the assessment of the difference between the participation and influence of children. Also, so far, the studies were mainly based on high involvement products. Again, the case of low involvement products presents a research gap and a direction for future research.
Second, this study focuses on children aged from 12 to 18, which allowed to avoid a moderating effect of children’s age in relationship with their participation and influence (to control results for the age). However, when the age of children varies in broad range, the influence of age should be considered (Castro et al., 2016). The assessment of the moderating effect of the age could be an important element for further research; it would broaden the understanding of the distinction and links between participation and influence and would contribute to the overall knowledge on the issue.
Third, this research evaluates children’s participation using the scale developed by Beatty and Talpade (1994). However, this scale measures children’s participation only in two stages, while the purchase decision–making process is typically described as a sequence of at least three or four stages. Future research should pay attention to more precise measurements of participation in every stage and direct or mediating relationships between the participation in these stages and the ability to influence.
In addition, the results show that children’s participation and influence act in entirely different ways. These results open a discussion about the significance of participation and the influence among all other members of a family purchasing process, as the distinction between participation and influence may be applied in regard to every member of the process. This becomes increasingly important, as the Internet and social media extend the consumers’ ability to find information about products and impact our behavior in need recognition, information search, and evaluation of alternatives.
However, the most promising direction of the nearest research should concentrate on the reasons that increase or decrease the difference between participation and influence measures. In other words, the key issue is to develop understanding on what makes the participation more or less “productive” in terms of its impact on the final decision. Most probably, large scale qualitative research would be the most logical approach for the first steps in this direction.
