Attributing Values to Devices

Abstract

This research examines anthropomorphism by testing the values that people attribute to electronic devices. We ask four main questions: Do people attribute human values to devices; Do devices differ in their value profiles; What underlies the attribution of values to devices; and Do individual and social differences affect these attributions. In Study 1, participants (N = 265) attributed Schwartz’s 10 basic human values to devices, reported their personal value priorities, as well as the frequency and difficulty in using each device. In Study 2, participants (N = 231) attributed values to devices, and responses were analyzed by age. Results show that people attribute human values to electronic devices, and that each device has a unique value profile. Anthropomorphizing devices reflects social consensus as to the symbolic meaning of each device, rather than projection of personal values or frequency of device use. Members of social groups share device values and differ from members of other groups.

Keywords

anthropomorphism values attribution electronic devices aging

People tend to imbue non-human objects with human-like characteristics, motivations, intentions, and emotions (Epley et al., 2007). Of particular interest is the tendency to assign minds to computers, gadgets, robots, and other electronic devices (e.g., Abubshait & Wiese, 2017; Nass & Moon, 2000; Reeves & Nass, 1996; Wang, 2017; Waytz et al., 2010a). Although it is easy to understand why we attribute human characteristics to animals (e.g., Serpell, 2003), the tendency to attribute these characteristics to electronic devices, which are undoubtedly artificial and lack a “soul” of their own, is less clear. It is possible that people attribute human characteristics to electronic devices because these devices are responsive to human activity. The mere fact that they do something may elicit the perception that they possess human-like characteristics, such as intentions. In this paper, we explore a specific aspect of this tendency, namely the attribution of human values to electronic devices. We focus on four main questions: (a) Do people attribute human values to devices; (b) Do devices differ in their value profiles; (c) What underlies the attribution of values to devices; and (d) Do individual and social differences affect the attribution of values to devices. To answer these questions, we use Schwartz’s (1992) and Schwartz et al. (2012) Theory of Basic Human Values. We begin with a brief presentation of the theory, and then review research on the attribution of human values to objects.

Human values are desirable, stable, and broad goals that vary in importance and guide our perception, judgment, and behavior (Schwartz, 1992; Schwartz et al., 2012). Schwartz’s Theory of Basic Human Values (Schwartz, 1992; Schwartz et al., 2012) identifies 10 broad values, groups them into four higher order types of values, and organizes them on two bipolar dimensions: self-enhancement versus self-transcendence, and openness to change versus conservation. The 10 broad values are structured on a circle, such that values that are positively correlated are adjacent to one another, while values that are negatively correlated are located at an opposite place around the circle. Thus, adjacent values share similar underlying motivations, whereas opposite values represent conflicting motivations. Figure 1 presents the value circumflex.

Figure 1.

The value circumflex.

Many studies have provided empirical support to the circumflex structure of human values, its universality, and its predictive validity (Sagiv & Roccas, 2017, 2022; Sagiv & Schwartz, 2022). As values guide human behavior, they should predict other behavioral outcomes in a systematic way. In terms of the associations between values and any outcome variable, this systematic organization means that if a value positively predicts an outcome to the highest degree, then the opposing value should have the most negative association with that outcome variable. The associations of the other values with that outcome should become progressively less positive and more negative as one moves around the value circle from the most predictive value to the adjacent value through the orthogonal value, and on to the opposite value. This sine-like wave pattern has been repeatedly demonstrated by visual presentation of the direction and magnitude of the correlations in their order around the circumflex (e.g., Kasser et al., 2002; Schwartz, 1992; Sortheix & Lönnqvist, 2014; Stieger et al., 2022; Zacharopoulos et al., 2016). Thus, any analysis of values attempts to find two features of attributed values—the circumflex configuration and the resulting sine-like pattern of associations between values and outcomes.

Many studies have examined the values that people endorse as relevant for themselves, but only few have focused on whether the theory of basic human values is relevant to objects (Allen et al., 2008; Shepherd et al., 2015; Torelli et al., 2012; Voorn et al., 2018). In these studies, participants usually report values of objects or brands. It is assumed that objects, and in particular brands, carry symbolic meaning that affects people’s behavior toward them (Aaker, 1997; Caprara et al., 2001; George & Anandkumar, 2018; Holbrook & Hirschman, 1982; Richins, 1994; Sweeney & Soutar, 2001), and that when participants are asked about values, they presumably reveal this symbolic meaning. Furthermore, congruency between personal values and one’s perceptions of brand values may influence behavior, for example, purchasing preferences (e.g., Allen, 2002; Malhotra, 1988; Shepherd et al., 2015; Zhang & Bloemer, 2008). Other studies have shown that self-brand congruency may reflect interaction with the brand, and may be similar to interactions with other people (Aggarwal & McGill, 2012), thus providing indication of anthropomorphism.

Focusing on the interaction between people and objects raises several questions. First, we may ask what content people use to animate objects. Some studies found that people assign traits to objects (Chandler & Schwarz, 2010; Sobel & Sims, 2020), as well as emotions (Delfabbro & Winefield, 2000; Waytz et al., 2010b), intelligence (Krach et al., 2008), and intentions (Airenti, 2018). It is yet unclear whether people attribute human values to devices.

Since people animate some objects but not others (Aggarwal & McGill, 2007), a second question is which objects are anthropomorphized. Some objects are designed to seem to have human organs, especially faces, and are therefore more easily anthropomorphized (Aggarwal & McGill, 2007; Labroo et al., 2008). Other objects sometimes “behave” in unpredicted ways (e.g., computers) and are therefore considered more human-like (Waytz et al., 2010b). In the current study, we examine anthropomorphism of electronic devices in search for similarities or differences across devices.

Third, we can ask how people attribute human-like characteristics to objects. Do people project their own characteristics onto objects (Benfield et al., 2007)? Or do they embrace socially accepted images of these objects and attribute to devices features that are congruent with these images?

Last, it is important to determine who tends to anthropomorphize objects more often. For example, some studies show that lonely people anthropomorphize objects more frequently (Bartz et al., 2016; Epley et al., 2008). Other studies show that individuals with preoccupied attachment style anthropomorphize objects more often than do participants with secured attachment style (Wang, 2017). In addition, people with a stable need for control tend to endorse anthropomorphic beliefs (Epley et al., 2008), and the same is true for individuals who were raised in families from high socioeconomic status (Whelan et al., 2019). People with conservative political views anthropomorphize more relative to those with liberal views (Chan, 2020). More generally, anthropomorphizing by itself may reflect a behavioral tendency (i.e., it may act as a trait, Waytz et al., 2010a). In that sense, we may ask whether people who prioritize specific values tend to anthropomorphize objects more than those who prioritize other values, or whether the frequency of device use affects the attribution of human values.

Do People Attribute Human Values to Devices?

Values are broad human goals. They reflect motivations that may or may not be expressed in behavior (Roccas et al., 2002). As such, they are similar to other human characteristics that may be attributed to non-human entities. In addition, values carry symbolic meanings that may be attached to objects. Among the symbolic meanings of objects reported in previous studies, two may be relevant when testing anthropomorphizing. First, objects may carry values of social connectedness and interpersonal ties (e.g., Richins, 1994; Sheth et al., 1991; Sweeney & Soutar, 2001). In fact, social connectedness is one of the basic needs that drives anthropomorphism (Christoforakos & Diefenbach, 2022; Epley et al., 2007; Yang et al., 2020). The value circumflex represents interpersonal ties through the self-transcendence pole that encompasses universalism and benevolence values, as well as through the conservation pole that includes conformity, tradition, and security values.

The second symbolic meaning of objects that may affect anthropomorphism relates to the basic need for certainty. Richins (1994) defined several categories of symbolic meaning of objects, including a utilitarian value (i.e., an object’s functionality and usefulness), as well as an identity and self-expression value (i.e., an object symbolizes personal history or feelings of competence). These categories reflect aspects of one’s life that instill certainty. Thus, people tend to anthropomorphize an object in order to predict its function and to increase their ability to make sense of it (Epley et al., 2007; Guthrie, 1995; Yang et al., 2020). Two clusters of values are relevant for achieving certainty. Conservation values (conformity, tradition, and security) help individuals manage uncertainty through reliance on social norms, authorities, and traditions. Self-enhancement values (power and achievement) help individuals handle uncertainty, and people who are high on these values seek control and attempt to dominate their social environment. Thus, the need for social connectedness may lead people to anthropomorphize electronic devices by attributing values of conservation and self-transcendence to objects, and the need for certainty may lead people to attribute values of conservation and self-enhancement to objects.

However, given that values are more abstract and less descriptive in nature, individuals may consider them quite human and may be reluctant to attribute them to objects. The difference between values and traits may help explain this point. Although values reflect broad motivations, traits refer to the way in which individuals tend to feel, think, and behave. Unlike traits, values are always desirable, and as they are a core part of identity, people see them as less malleable to change (Roccas et al., 2014). Since traits are descriptive in nature, they can be easily applied to objects. For example, previous studies have shown that people attribute agentic traits to cars (Chandler & Schwarz, 2010), and traits of conscientiousness and openness to computers (Sobel & Sims, 2020). It might be much harder to imbue objects with values than with traits, although this is the essence of branding. Voorn et al. (2018) compared the impact of the values and the traits associated with brands, and found that values were more important than traits in terms of the long-lasting commitment to the brand. This effect may explain why it is so important for advertisers to understand how to imbue brands with values.

Nevertheless, it is important to differentiate between strong and weak forms of anthropomorphism. Epley et al. (2007) argued that “strong forms of anthropomorphism entail behaving as if a non-human agent has humanlike traits or characteristics along with explicit endorsement of those beliefs (such as with religious agents), whereas weaker forms may only entail ‘as if’ metaphorical reasoning (such as with one’s malevolent computer)” (p. 867). Given the nature of human values described above, we address a weak form of anthropomorphism, focusing on how different devices reflect the symbolic meaning of values.

For the purpose of the current study, we look at the value circumflex and the sine-like pattern as measures of imbuing devices with values. We assume that finding the value circumflex as well as the sine-like pattern obtained in studies of human values also in the values that people attribute to objects will demonstrate significant anthropomorphism.

Do Devices Differ in Their Value Profiles?

To the best of our knowledge, no previous research has examined the attribution of human values to electronic devices. Although some studies tested values of brands (Allen et al., 2008; Caspi et al., 2022; Shepherd et al., 2015; Torelli et al., 2012; Voorn et al., 2018), they did not report the configuration of these values. Furthermore, these studies tested values of brands while the current study tests devices. People attach emotional or practical values to brands because brands are designed to trigger these feelings. In contrast, we focus on attributing values to objects, regardless of marketing and branding efforts.

Waytz et al. (2010b) suggested that people anthropomorphize unpredictable objects more than they anthropomorphize predictable objects. Thus, people may assign electronic devices that function in an unpredictable way (e.g., computers or smartphones) more human-like characteristics than they will assign to electronic devices that function more predictably (such as microwaves or landline phones, which have a single and clearly predictable function).

What Underlies the Attribution of Values to Devices?

We suggest that the values that people attribute to objects could rely on two alternative sources. First, individuals may assign their own value priorities to objects. As people project their desires, feelings, wishes, and other characteristics onto other people, they may do so for objects as well (Boddy, 2005; Dichter, 1964). In fact, some authors have defined anthropomorphic thoughts as the projection of human characteristics onto non-human targets. Heider (1964) observed that in order to reduce uncertainty and achieve a sense of order, people project human-like beliefs onto non-human objects. Benfield et al. (2007) found positive correlations between participants' self-reported personality traits and the personality traits that they attributed to their car. Thus, a person who values power more than benevolence may attribute power to objects more often than benevolence. In such a case, we will expect to find correlations between personal values and object values. Nevertheless, the anthropomorphism literature does not argue that the attributed characteristics stem from the characteristics of the person who interacts with the object. Thus, while it is possible that people will attribute the values that they endorse for themselves to objects, this possibility must be tested empirically.

Alternatively, attributed human values may reflect socially shared “images” of objects (Allen, 2002; McCracken, 1986; Shavitt, 1990; Solomon, 1986). If people attribute socially shared values to objects, there should be no correlations between personal and attributed values. Instead, there should be a relative consensus within social groups concerning any given object, with some differences across groups or cultures.

Do Individual and Group Differences Affect the Attribution of Values to Devices?

Although personal values may be the source of the values that people attribute to objects, it is also possible that the level of interaction with an object, or its usage, affects anthropomorphism. Epley et al. (2007) argued that members of modern cultures, as opposed to members of non-industrialized cultures, interact with mechanical devices (e.g., cars and computers) quite frequently, and are therefore less likely to anthropomorphize these devices. More generally, if one does not interact with an object, the chances of developing an accurate cognitive representation for that object decreases, thus enhancing the tendency to attribute human characteristics to such objects. Therefore, people who use a device less frequently will anthropomorphize it more than people who utilize it more frequently, and devices with scarce usage will lead to more anthropomorphism than devices whose usage is prevalent.

In addition, previous studies have documented small but significant differences in anthropomorphism between social groups (Letheren et al., 2016; Tan et al., 2018; Whelan et al., 2019). Prominent social groups that may differ in their attitudes toward electronic devices are groups of different ages. First, Letheren et al. (2016) found that younger adults tend to anthropomorphize more than older adults do. Second, previous studies of human values reported that they were age-dependent (e.g., Fung et al., 2016; Robinson, 2013). Most importantly, there are differences in the use of electronic devices between generations. Older adults tend to adopt technology more slowly than the general adult population (Anderson & Perrin, 2017), and they use fewer types of technologies than do younger adults (Olson et al., 2011). Furthermore, there are differences between younger and older adults in how they interact with technologies (Kang & Yoon, 2008), depending in part on the domain of technology (Olson et al., 2011). We suggest that social groups may differ in the values that they attribute to devices, due to differences in the tendency to anthropomorphize, differences in value priorities, and differences in device use.

We report two studies. Study 1 attempts to answer our first and second questions by testing whether the value circumflex and the sine-like pattern typical of personal values emerge also when people attribute values to electronic devices. Finding that each device has a unique value profile will provide evidence for social consensus regarding any given device, as well as for differences between devices. Study 1 also examines our second and third questions by looking at the relations between personal values and the frequency and difficulty of use of each device on the one hand and the values of each device on the other hand. Study 2 examines our last question by focusing on group differences, testing differences between younger and older adults.

Study 1

Method

Participants

The sample included 265 participants (age range: 19–64, M = 30.8, SD = 9.4; 81% born in Israel; 80% women), who received academic credit for participation as part of their undergraduate degree at the Department of Psychology of an Israeli university. Given that our design is correlative, we intended to have at least 250 participants, (Schönbrodt & Perugini, 2013).

Instruments and Procedure

Materials and data for Studies 1 and 2 are available online at https://osf.io/tpjr4/?view_only=a1ca0086c2c44bca9c039df07ca9ad46

Measuring Personal Values: Schwartz Value Survey (SVS)

The SVS consists of 46 items representing 10 basic values. Participants rated the importance of each value as a guiding principle in their life on a 9-point scale, ranging from −1 (opposed to my values), 0 (not important), 3 (important), 6 (very important), to 7 (of supreme importance). For an explanation of this response format, see Schwartz (1992). Participants filled the SVS at the beginning of the study, and then took part in other unrelated studies before reporting device values.

Measuring Device Values: Symbolic Values of Device (SVD) Questionnaire

This questionnaire referred to 10 devices: a personal computer (PC), a laptop computer, a tablet, a smartphone, a television (TV), a treadmill (running machine), a landline phone, an Automatic Teller Machine (ATM), a microwave, and a washing machine. We did not attempt to encompass all types of devices systematically. Rather, we selected electronic devices that are used indoor or outdoor, devices that are used for communication with others or for personal use, devices that have a single function or multiple functions, and devices that involve newer or older technology. The order of device presentation was random.

For each device, participants were asked to rate the degree to which it reflected each of the 10 values by using the adapted Short and Broad SVS questionnaire (Lindeman & Verkasalo, 2005; Roccas et al., 2017; Sekerdej & Roccas, 2016). In the original questionnaire, each value item consists of the broad definition of that value type. For example, the item for security reads “Safety, stability, and order. Caring of the family's security and its health.” The SVD scale was similar to the SVS scale, and ranged from −1 (the device reflects the opposite value), 0 (the device does not reflect the value), 3 (the device reflects the value), 6 (the device reflects the value to a large extent), to 7 (the device highly reflects the value). There was one questionnaire for each device.

Frequency and Difficulty of Use

After completing the SVD questionnaire, participants were asked to rate how often they used each device on a 1 (not at all) to 7 (very often) Likert scale, and to rate the difficulty of use of each device on a 1 (very easy) to 7 (very difficult) Likert scale.

Results

Do People Attribute Human Values to Devices?

To look for the circumflex configuration, we ran a multidimensional scaling (MDS) analysis, using a theory-based starting configuration as in Bilsky et al. (2011) (see also Bilsky et al., 2015). For each device, we conducted a separate analysis. Figure 2 presents the results. Red markings show that a value deviated from the expected theoretical configuration.

Figure 2.

Multidimensional scaling analyses of attributed values, by device (Study 1). Dashed lines represent borders between the four higher-order dimensions. Note. UN = universalism; BE = benevolence; CO = conformity; TR = tradition; SE = security; HE = hedonism; PO = power; AC = achievement; ST = stimulation; SD = self-direction.

Generally, the expected value structure emerged for all devices, supporting the notion that people use human values to anthropomorphize electronic devices. There were some deviations within dimensions. For example, for some devices values of security were closer to self-transcendence values than to self-enhancement values. The value structure for TV was an exception, as it differed markedly from the predicted structure.

Next, we looked for sine-like patterns, analyzing frequency and difficulty of using each device as dependent variables. The average correlation between the reported frequency of device use and its difficulty of use was negative (average r = −0.165, see Table 1).

Table 1.

Correlations Between the Values That People Attribute to Devices and the Frequency of Use of Each Device in Study 1.

	UN	BE	CO	TR	SE	PO	AC	HE	ST	SD	Mean	StD	r _use,dif
PC	0.026	0.027	0.013	−0.192**	0.049	0.053	−0.076	0.035	0.023	0.088	3.41	2.32	−0.050
Laptop	−0.023	0.010	−0.113	−0.128*	0.010	−0.012	0.039	0.065	0.011	0.165**	5.50	1.91	−0.250**
Tablet	0.172**	0.174**	−0.082	−0.092	0.079	−0.018	−0.016	−0.103	−0.015	−0.041	2.47	2.03	−0.172**
Smartphone	−0.124*	0.159**	0.035	−0.019	0.030	0.002	−0.072	0.053	−0.119	0.038	6.59	1.11	−0.176**
TV	0.060	−0.022	−0.145*	−0.065	−0.033	0.088	0.031	−0.079	−0.035	0.222**	4.42	2.12	−0.127*
Treadmill	0.045	0.016	−0.089	−0.035	0.069	−0.096	−0.058	0.077	0.081	0.002	2.37	1.86	−0.233**
Landline phone	0.046	−0.025	0.028	−0.090	0.002	−0.011	0.094	0.006	−0.013	0.01	2.02	1.56	0.010
ATM	−0.088	0.135*	−0.117	−0.099	−0.018	−0.073	0.024	0.091	0.084	0.071	4.50	1.88	−0.074
Microwave	0.103	0.123*	−0.188**	−0.045	0.130*	−0.052	−0.123*	−0.012	−0.134*	0.136*	4.88	2.08	−0.126*
Washing	−0.110	−0.028	−0.038	−0.055	0.139*	−0.015	0.016	0.105	−0.012	−0.020	5.65	1.79	−0.452**

* p < .05; ** p < .001 Note. UN = universalism; BE = benevolence; CO = conformity; TR = tradition; SE = security; HE = hedonism; PO = power; AC = achievement; ST = stimulation; SD = self-direction; r_use,dif = correlation between reported use and reported difficulty.

Tables 1 and 2 present the correlations between the values that people attributed to devices, the reported frequency of use, and the difficulty of use. Most correlations were not significant. However, there was a significant positive correlation between values of self-transcendence (benevolence and universalism) and values of openness (stimulation and self-direction) on the one hand, and reported frequency of use and difficulty of use on the other hand. In contrast, a negative correlation emerged between the values of conservation (conformity, tradition, and security) and the reported frequency of use as well as the difficulty of use.

Table 2.

Correlations Between the Values That People Attribute to Devices and the Difficulty of Use of Each Device in Study 1.

	UN	BE	CO	TR	SE	PO	AC	HE	ST	SD	Mean	StD
PC	0.076	0.051	0.048	−0.013	0.069	0.010	0.009	−0.135*	0.010	−0.092	1.60	1.21
Laptop	0.003	−0.003	0.054	−0.060	−0.037	0.035	0.129*	−0.075	0.023	−0.034	1.65	1.29
Tablet	−0.122	−0.072	−0.041	−0.135*	0.036	0.110	0.040	−0.011	0.165**	0.029	1.82	1.35
Smartphone	−0.085	−0.034	0.015	−0.006	−0.044	0.062	0.069	−0.039	0.118	−0.090	1.45	1.10
TV	−0.039	−0.014	0.083	−0.012	−0.030	−0.031	−0.035	0.030	0.112	−0.066	1.50	1.22
Treadmill	0.147*	0.010	−0.017	−0.040	0.027	0.006	−0.020	−0.190**	−0.033	0.172**	2.03	1.49
Landline phone	0.005	−0.037	−0.025	0.011	−0.060	0.054	0.062	−0.074	0.029	0.053	1.45	1.24
ATM	0.028	0.050	0.032	−0.046	−0.075	0.055	0.007	−0.102	0.138*	−0.081	1.43	1.12
Microwave	−0.087	−0.094	−0.125*	0.129*	−0.061	0.120	0.055	−0.005	0.109	−0.026	1.34	1.00
Washing	−0.036	0.043	−0.005	0.017	0.005	0.005	0.034	−0.012	0.029	−0.032	1.93	1.50

* p < .05; ** p < .001 Note. UN = universalism; BE = benevolence; CO = conformity; TR = tradition; SE = security; HE = hedonism; PO = power; AC = achievement; ST = stimulation; SD = self-direction.

For each device, the frequency of use and the difficulty of use exhibit positive (or negative) correlations with at least one of the value types (see rows in Tables 1 and 2). The circumflex model predicts that correlations should gradually become less positive (or less negative) and more negative (or more positive) around the circular model. This pattern should follow a sine wave once plotting the 10 value types on the x-axis of a Cartesian coordinate system, and the strength of the correlation on the y-axis (Kasser et al., 2002; Schwartz, 1992; Sortheix & Lönnqvist, 2014; Stieger et al., 2022). Supplementary Figure S1 (frequency of use) and Supplementary Figure S2 (difficulty of use) depict the correlations between personal values and the values that people attribute to objects on the one hand, and the correlations with the frequency and difficulty of use on the other hand, along with their correspondent sine functions.

As Supplementary Figures S1 and S2 illustrate, the relations between personal and attributed values and the frequency and difficulty measures follow a clear sine function for some devices, whereas for other devices the sine function is less apparent. In addition, the sine functions for personal values and the sine functions for the values that people attribute to objects do not overlap. Together, these findings show that attributed values approximately follow the predictive sinusoidal relation with outcome variables. This result further supports the idea that people anthropomorphize objects by imbuing them with human values.

Do Devices Differ in Their Value Profiles?

The MDS analysis suggested that the values that people attribute to devices closely replicate the value circumflex of human values. However, since the analysis looked at each device separately, it does not indicate whether value profiles differ across devices. To determine whether devices have unique symbolic meanings, devices should show distinct value profiles. Figure 3 presents the average value scores by devices. Inspection of this figure reveals the differences as well as the similarities between devices. For example, it appears that people attribute values of conservation and self-transcendence to a washing machine, and values of openness to change and self-enhancement to a TV. People attribute the value of hedonism to devices that are associated with entertainment, but they attribute the value of security to a washing machine, an ATM, and a landline phone. Beyond some specific similarities, it seems that participants attribute different values to different devices, supporting the idea that each device possesses a specific value profile. These findings may also suggest the existence of social consensus regarding the values that characterize each device. Importantly, we observed no devices whose profiles involved equal attributions of all values. Ten repeated measures ANOVAs, one for each device, confirmed this observation. For all devices, the effect of values was significant, F (9, 2277) ≥ 23.463, p’s < .001, partial η² ≥ .084. Therefore, participants had no difficulty anthropomorphizing electronic devices by imbuing them with human values that they considered relevant. Furthermore, we expected that people would anthropomorphize devices with several functions that may “behave” unpredictably more easily than devices with a single function that operate more predictably. The results did not indicate such a pattern. Instead, we found differences in the attributed values: people attributed openness and self-enhancement values to multifunctional and unpredictable devices (e.g., PC, laptop, and smartphone), and assigned conservation and self-transcendence values to predictable devices with a single function.

Figure 3.

Average value scores by device. Note. UN = universalism; BE = benevolence; CO = conformity; TR = tradition; SE = security; HE = hedonism; PO = power; AC = achievement; ST = stimulation; SD = self-direction.

What Underlies the Attribution of Values to Devices?

Projection of Personal Values

Table 3 presents the correlations between personal values and the values that people attributed to devices. We present only the correlations between corresponding personal values and device value (e.g., personal universalism and universalism attributed to a TV), and not the full correlation matrix. Most correlations were not significant, and the few significant correlations were low (∼0.20 > r > ∼−20). Thus, it seems that personal values do not drive the attribution of values to objects, and people do not project their own values onto electronic devices.

Table 3.

Correlations Between Personal Values and the |Values That People Attribute to Devices in Study 1.

	PC	Laptop	Tablet	Smartphone	TV	Treadmill	Landline Phone	ATM	Microwave	Washing
UN	0.120	0.088	0.038	−0.011	0.095	−0.171**	0.055	0.078	0.017	0.000
BE	−0.006	−0.055	−0.088	−0.021	−0.121	−0.084	−0.039	−0.029	−0.062	−0.028
CO	0.122	0.097	0.148*	−0.054	−0.071	0.068	−0.072	0.100	−0.122	0.081
TR	0.121	0.123	0.165*	0.068	−0.103	0.134*	−0.001	0.066	0.027	−0.058
SE	0.105	0.076	−0.022	−0.009	0.075	0.034	0.093	−0.131*	−0.035	0.020
PO	−0.011	−0.038	−0.042	−0.048	−0.122	−0.029	0.050	0.049	0.009	−0.064
AC	0.020	0.069	−0.037	−0.123	−0.042	0.225**	−0.080	−0.137*	−0.091	−0.107
HE	0.034	0.170**	−0.001	−0.032	−0.088	0.054	0.100	0.022	−0.106	0.042
ST	0.068	0.093	−0.028	−0.084	−0.101	0.025	0.026	0.013	0.050	0.036
SD	0.131*	0.024	0.120	0.093	−0.024	0.016	0.031	−0.026	−0.005	0.003

* p < .05; ** p < .001 Note. UN = universalism; BE = benevolence; CO = conformity; TR = tradition; SE = security; HE = hedonism; PO = power; AC = achievement; ST = stimulation; SD = self-direction. The table presents only corresponding personal-device values.

Individual Differences in Value Priorities

We tested individual differences by looking at participants who were low or high in conservation values (conformity, tradition, and security). First, we split participants into low (below median) versus high (above median) on each of these three values, and then we ran 30 separate mixed analyses of variance (10 devices, three values), with personal value (i.e., conformity, tradition, and security) as a between-subject variable and all 10 attributed values as a within-subject variable. We focus on the interactions between personal values and attributed values. We predicted that participants who are low in conformity, tradition, or security will use less extreme attributions than will participants who are high on these values, but the analyses did not confirm these predictions. Twelve of the 30 interactions reached significance (ps ≤ .043). Only one analysis found that participants who were high on a given value (security) attributed more extreme values to the device (washing machine). Supplementary Table S1 presents the full results, and Supplementary Figure S3 presents the interactions between personal values and attributed values for the washing machine. Thus, in contrast to prior studies, these analyses show that motivations of connectedness and certainty do not influence the attribution of human values to electronic devices.

Interaction with Devices: Individual Differences in Usage

We conducted two analyses to test the prediction that low frequency of using a device increases the attribution of human characteristics to that device. First, we classified devices as low or high according to participants' reports of the frequency of use, and compared the least frequently and most frequently used devices. The least frequently used devices were tablet, treadmill, and landline phone (means <3.0, see Table 1). As can be seen in Figure 3, the value profiles of the least frequently used devices resembled those of the most frequently used devices.

In addition to the analysis that compared devices, we conducted a within-device analysis to test the same prediction, comparing the attributions of individuals who reported low (below median for that device) or high (above median for that device) frequency of use for any given device. For each device, we ran a mixed analysis of variance (ANOVA), with the attributed value as a within-subject variable and usage as a between-subjects variable. The main effect of the attributed value was significant in all analyses, Fs (9, 2241) ≥ 24.647, p’s < .001, partial η²’s ≥ .009. There was no significant main effect of usage, Fs < 1. Out of the 10 interactions, only two were significant (ATM: F (9, 2231) = 2.107, p = .026, partial η² = .008; Microwave: F (9, 2268) = 2.178, p = .021, partial η² = 0.009). Thus, the attribution of human values to devices did not reflect the frequency of using each device. Supplementary Table S2 and Supplementary Figure S4 present the mean value scores by device usage.

Discussion

Study 1 shows that Schwartz’s (1992) Theory of Basic Human Values provides an accurate description of the symbolic meanings (i.e., values) that people attribute to devices. We found that the general configuration of device values resembled the general configuration of personal values. Furthermore, each device appeared to have a unique value profile, implying that the device carries a symbolic meaning of its own. Our findings suggest that people attribute human values to devices (our first research question) and that devices differ in their value profiles (our second question). We tested two potential factors that underlie the attribution of values to devices (our third research question): personal value priorities and individual differences in usage. Personal value priorities did not associate with the values that people attributed to devices. In addition, we found that devices that were used least frequently did not differ in their value profiles from devices that were used most frequently, and there was no difference in value attribution between individuals who used the device less or more frequently. Thus, the level of usage of an electronic device was not related to anthropomorphism.

Our findings further indicate that individuals do not project their own values onto devices, and that personal usage does not determine the attribution of values, at least for the devices presented in the current study. Instead, electronic devices may have socially accepted values, and different people may share these accepted values. If neither personal values nor frequency of use affect value attribution, perhaps social consensus regarding the symbolic meaning of devices underlies the tendency to anthropomorphize objects through the attribution of human values. Study 2 examines the possibility that different social groups differ in the values that they attribute to devices.

Study 2

Study 2 aimed to (1) replicate some of the findings of Study 1, and (2) test whether the values that people attribute to objects differ across social groups. To examine group differences in attributed values, we compared younger and older participants. Previous studies of human values found differences between age groups (e.g., Fung et al., 2016; Robinson, 2013). The tendency to anthropomorphize non-human entities also differs between younger and older adults (Letheren et al., 2016), and it may associate with age differences in the level of interaction with electronic devices (Kang & Yoon, 2008; Olson et al., 2011). In Study 1, we found that personal values do not correlate with device values. If different social groups prioritize values differently, we may find that younger and older adults assign different values to electronic devices.

Method

Participants

The sample included 231 participants, recruited through an Israeli online panel. There were 118 participants between age 22 and age 41 and 113 participants between age 67 and age 85. Table 4 presents demographic details of the sample. Sample size was determined by the availability of older participants, to which a similar number of younger participants was matched.

Table 4.

Demographic Characteristics of Participants in Study 2.

	%
Education
High school or less	23.0
Non-academic continuing education	25.5
BA	35.5
MA or PhD	16.0

Socio economic status
No income	0.4
Very low	18.2
Low	15.6
Average	38.5
High	16.0
Very high	11.3

Religion
Secular	60.6
Traditional (Masorati)	21.6
Orthodox	11.3
Ultra-orthodox	6.5

Instruments and Procedure

Symbolic Values of Device Questionnaire

We used six devices from the original SVD questionnaire, including only devices that serve for communication: a personal computer (PC), a laptop computer, a tablet, a smartphone, a TV, and a landline phone. Devices were presented in a random order.

Frequency of use and difficulty of use were measured with the same Likert scales that appeared in Study 1.

Results

Replication of the Structural Configurations and Sine-Like Patterns Documented in Study 1

To replicate the results of Study 1 regarding the structural configuration of device values in the new sample, we first ran MDS analyses for each device, using the same method as in Study 1. Figure 4 presents the results of these analyses.

Figure 4.

Multidimensional scaling analyses of attributed values, by device (Study 2).

Overall, we found the expected circumflex structure as we did in Study 1, with some small deviations within the higher-order dimensions. Three devices (laptop, tablet, and landline phone) did not show the expected circumflex structure, and seemed to have only one higher-order dimension—self-transcendence and conservation versus self-enhancement and openness. This pattern corresponds to the classification of the 10 values into communal versus agentic values (Trapnell & Paulhus, 2012), also showing the individualism-collectivism distinction (Oishi et al., 1998). Results for the three other devices (TV, PC, and smartphone) revealed the expected value circumflex configuration. Next, we tested the correlations between the values that people attribute to devices, device frequency of use, and device difficulty of use. Tables 5 and 6 present the data (see also the Supplementary Tables S3 and S4 for presentation by age groups), and Supplementary Figure S5 shows the sinusoidal pattern for each device separately for younger and older participants. Thus, the results partly replicated Study 1, providing further support to our main tenet that people anthropomorphize electronic devices through the attribution of human values.

Table 5.

Correlations Between the Values That People Attribute to Devices and the Frequency of Use of Each Device in Study 2.

	UN	BE	TR	CO	SE	PO	AC	HE	ST	SD
PC	−0.013	−0.035	−0.157*	−0.039	−0.007	0.004	0.020	0.062	0.152*	0.054
Laptop	−0.098	0.000	−0.081	−0.022	0.026	0.070	0.016	0.114	−0.110	0.066
Tablet	−0.038	−0.043	−0.027	0.079	0.085	0.028	0.017	−0.023	0.005	−0.070
Smartphone	−0.039	0.108	−0.099	−0.070	0.036	−0.038	−0.131*	0.087	−0.012	0.187**
TV	0.125	0.045	0.002	−0.034	0.109	−0.039	−0.078	−0.124	−0.047	0.106
Landline phone	0.090	−0.059	−0.106	−0.042	−0.062	0.029	−0.063	0.068	0.103	0.139*

Table 6.

Correlations Between the Values That People Attribute to Devices and the Difficulty of Use of Each Device in Study 2.

	UN	BE	TR	CO	SE	PO	AC	HE	ST	SD
PC	−0.089	0.034	0.152*	0.020	−0.037	0.078	−0.059	−0.107	−0.046	−0.012
Laptop	0.026	0.013	−0.078	0.023	−0.015	0.001	−0.018	−0.031	0.061	0.025
Tablet	0.047	0.158*	0.083	0.049	0.084	−0.001	−0.129	−0.108	−0.065	−0.083
Smartphone	0.022	−0.008	0.070	0.050	0.036	0.016	0.042	−0.071	−0.070	−0.112
TV	0.015	0.009	−0.055	−0.068	0.030	0.012	0.013	0.028	0.110	−0.114
Landline phone	0.104	−0.098	−0.129	−0.016	0.042	0.077	0.063	0.021	0.094	−0.058

* p < .05; ** p < .001 Note. UN = universalism; BE = benevolence; CO = conformity; TR = tradition; SE = security; HE = hedonism; PO = power; AC = achievement; ST = stimulation; SD = self-direction.

To look at the effects of age on attribution of human values, we first tested the correlations of age with usage, with an attempt to examine possible confounds. Age correlated positively with using PC, TV, and landline phone (rs = 0.309, 0.265, and 0.452, respectively, all ps < .001), and negatively with using a laptop (r = −0.359, p < .001).

To test the effects of usage on attribution of human values to devices, we analyzed the frequency of use of each device. Participants reported that they used the landline phone less often than they used all other devices (Mean = 2.84, SD = 1.97), followed by tablet (Mean = 2.91, SD = 2.00). The reported usage of these devices was below the middle of the scale, and we considered them as representing the least frequently used devices. The reports of using the other devices were above the middle of the scale, in ascending order: laptop (Mean = 4.51, SD = 2.38), PC (Mean = 4.55, SD = 2.67), TV (Mean = 5.16, SD = 2.07), and smartphone (Mean = 6.17, SD = 1.62). A repeated measures ANOVA confirmed that usage differed between devices, F (5, 1160) = 98.160, p < .001, partial η² = .297. Post-hoc pairwise comparisons with Bonferroni correction showed significant differences between all devices (ps ≤ .044), except landline phone versus tablet and laptop versus PC that were not significant. Inspection of Figures 5 and 6 reveals that the profile of the landline phone differed markedly from the profiles of all other devices, while the tablet profile did not differ from all other profiles.

Figure 5.

Average value scores by device, separated by usage. Low – low frequent users, high – high frequent users. Note. UN = universalism; BE = benevolence; CO = conformity; TR = tradition; SE = security; HE = hedonism; PO = power; AC = achievement; ST = stimulation; SD = self-direction; SMTP = Smartphone; LLP = Landline phone; TAB = Tablet; LAP = Laptop.

Figure 6.

Average value scores by device, separated by age. Note. UN = universalism; BE = benevolence; CO = conformity; TR = tradition; SE = security; HE = hedonism; PO = power; AC = achievement; ST = stimulation; SD = self-direction; SMTP = Smartphone; LLP = Landline phone; TAB = Tablet; LAP = Laptop.

Figure 5 presents the results of a second analysis that compared participants who reported low (below median) and high (above median) frequency of use. For each device, we ran a mixed ANOVA, with value as a within-subject variable and usage as a between-subject variable. The main effect of value was significant in all analyses, Fs (9, 1998) ≥ 9.917, p’s < .001, partial η²’s ≥ 0.004. There was no significant main effect of usage, Fs < 1. Out of the six interactions, only the interaction for smartphone reached significance, F (9, 1998) = 2.747, p = .003, partial η² = 0.012. Relative to individuals who reported that they used smartphones less frequently, those who reported higher frequency of use rated smartphones as reflecting less conformity, t (228) = 2.221, p = .045, and less achievement, t (225) = 2.250, p = .026, but more self-direction, t (227) = −3.633, p < .001.

These analyses corroborate the results of Study 1 and show that the frequency of use does not account for the values assigned to electronic devices.

Do Younger and Older Adults Differ in the Values That They Attribute to Devices?

To determine whether younger and older adults differ in their value attribution, we calculated the average score for each value, separately for each device and for each age group (see Figure 6). A marked difference appeared between the landline phone and all other devices, replicating the results of Study 1. Some age differences emerged as well. To test these differences, we ran six mixed ANOVAs, separately for each of the six devices, with the attributed value as a within-subject variable and age group as a between-subject variable. The main effect of the attributed value was significant in all analyses, Fs (9, 1962) ≥ 9.594, p’s < .001, partial η²’s ≥ .042. The main effect of age group was not significant, Fs < 1. We were especially interested in the interactions, and found four of them to be significant (see Table 7), along with the significant simple effects.

Table 7.

Interactions Between Values and Age Groups, by Devices.

Device	Interaction Effect	Significant Simple Effects
Laptop	F (9,1962) = 2.719, p = .004, partial η² = 0.012	ST: t (222) = −1.944, p = .047	Y < O
		PO: t (221) = 2.167, p = .031	Y > O
		HE: t (223) = 2.465, p = .014	Y > O
Tablet	F (9,1962) = 2.873, p = .002, partial η² = 0.013	SE: t (225) = −3.221, p = .001	Y < O
		HE: t (224) = 2.382, p = .018	Y > O
		TR: t (223) = −1.950, p = .052	Y < O
		PO: t (222) = 2.572, p = .011	Y > O
Smartphone	F (9,1971) = 4.199, p < .001, partial η² = 0.019	HE: t (223) = 3.215, p = .001	Y > O
Smartphone	F (9,1971) = 4.199, p < .001, partial η² = 0.019	PO: t (224) = 4.110, p < .001	Y > O
Landline phone	F (9,1944) = 2.339, p = .013, partial η² = 0.011	SD: t (223) = −2.652, p = .009	Y < O
Landline phone	F (9,1944) = 2.339, p = .013, partial η² = 0.011	HE: t (219) = −2.537, p = .012	Y < O

Note. Y = young; O = old; TR = tradition; SE = security; HE = hedonism; PO = power; ST = stimulation; SD = self-direction.

The interactions showed that younger participants rated most devices as more hedonic than did older participants, with the exception of the landline phone that younger participants rated as less hedonic than did older participants. In addition, younger participants rated the laptop as less stimulating than older participants did. They also attributed more power to the laptop, the tablet, and the smartphone relative to older participants. Younger participants rated the tablet as expressing security less often than did older participants. Younger participants also rated the tablet as less traditional and the landline phone as less self-directed relative to the ratings that the older participants provided. Thus, as predicted, social groups (younger and older adults) differed in the distribution of values that they attributed to different devices. Despite these specific value-age interactions, the general value profile of each device had a similar pattern across the two groups.

Discussion

In Study 2, we replicated most of our findings from Study 1 in a new age-diverse sample. In addition, Study 2 showed age differences in the distribution of the attributions of human values to different devices.

In general, younger participants chose hedonism more often than did older participants. Age and usage were correlated for most devices, but the analysis of usage did not show systematic differences between devices that were used least and most frequently or between frequent and infrequent users.

We interpret these results as evidence for within-group social consensus regarding the symbolic meanings of devices, which serves to anthropomorphize them. Taken together, the findings show that social groups defined by age attribute different values to electronic devices. Since we focused on devices that differed in their technological novelty and communication function, it is not surprising to find such differences between age groups.

General Discussion

The two studies presented here show that people anthropomorphize electronic devices by attributing human values to them, and that devices differ in their value profiles. Furthermore, the underlying mechanism that drives value attribution reflects neither the projection of personal values nor the frequency of use, but instead it is related to within-group social consensus regarding the symbolic meaning that each device reflects. Specifically, we found that younger and older adults attribute different values to electronic devices.

The fact that objects have symbolic meanings is not a new idea (Holbrook & Hirschman, 1982; Levy, 1959; Richins, 1994; Sweeney & Soutar, 2001; Torelli et al., 2012). The novel contribution of the current study is the systematic application of a full set of values from Schwartz’s (1992) and Schwartz et al. (2012) theory onto a relatively broad array of electronic devices. Previous studies that applied ideas derived from this theory to objects (in fact, brands) have rarely used the full set of values. We show that people attribute values to objects in a similar manner as they report their own personal values, so that the value circumflex configuration emerges in both cases. Additionally, using frequency and difficulty of use as dependent variables, we obtained an approximation of the sinusoidal curve pattern predicted by Schwartz’s theory. We interpret these findings as evidence for the ability to imbue inanimate objects with human values. Given that values are abstract and less descriptive in nature, they are not the most obvious candidates for the process of anthropomorphism. And yet, our participants attributed human values to electronic devices, and their pattern of responses was similar to the pattern of responses that involves humans.

Note that we asked participants to rate the degree to which each device reflected each of the 10 values. Such a reflection may arise from anthropomorphizing the device (washing machine is confidence, stable, and concern for the family’s security and health, i.e., the secure value) or from the social discourse regarding the device (washing machine metaphorically symbolizes security). By definition, human values carry symbolic meanings. Given that we employed a value attribution procedure that cannot differentiate between the above two sources for reflection, it is difficult to determine whether the results support strong or weak forms of anthropomorphism. It is possible that the attribution of human values to some devices stemmed from a stronger form of anthropomorphism, while the attribution of human values to other devices stemmed from a weaker form. Anthropomorphism characterized both cases, and their separation awaits further research.

Using the full set of values to refer to objects makes it possible to create a value profile for any given device. Such portrayal demonstrates similarities and differences between devices, as well as similarities and differences across social groups. Take, for example, the landline phone and smartphone. These two devices share one function—connecting people by affording vocal conversation. However, smartphones have many other functions, such as texting, navigating, watching movies, playing, or checking the time. Indeed, participants attribute different values to these two devices across our two samples and independent of their social group. Although they assign the landline phone values of self-transcendence and conservation, they assign the smartphone values of self-enhancement and openness. Differences in the same direction, although less prominent, were found between the PC and the laptop computer. Again, these two devices share similar functions, but hold different symbolic meanings. These findings contribute to the accumulated data concerning the tendency to anthropomorphize smartphones (e.g., Park & Kaye, 2019; Wang, 2017) or computers (e.g., Epley et al., 2008; Shin & Kim, 2020), by exposing specific value profiles that differentiate between devices that share some functionalities. The value theory allows us to better understand not only the attribution of human characteristics to specific devices but also the underlying mechanisms of anthropomorphism.

We found no support to the argument that individuals who use devices less frequently anthropomorphize more than individuals who use devices more frequently. We contend that social consensus regarding the symbolic meaning of electronic devices serves as a main source for anthropomorphizing them. Admittedly, the method that we employed for value attribution forced participants to attribute values, with no option to respond that the device carries no values. Nevertheless, there was consensus regarding the values that each device carries, regardless of individual differences in either value priority or in usage. It is also possible that the interaction with devices (that we operationalized as usage) is not the crucial factor for developing the cognitive representation of the devices that affects anthropomorphism in industrialized cultures. Since all members of the sampled culture are well familiar with the devices that we examined, they may all have similar cognitive representations of the devices despite differences in usage level.

Age differences in value attribution emerged on different devices. Consider, for example, the attributions that younger and older participants assigned to the laptop. Younger participants use laptops more often than do older participants, at least according to self-reports in the current sample. Not surprisingly, then, younger participants attributed values of self-enhancement to laptops, whereas older participants attributed values of openness to this device. More specifically, younger adults saw laptops as expressing power, while older participants saw them as expressing stimulation. These findings fit well with previous suggestions that older adults associate technology with younger age, especially when the technology is less familiar (Caspi et al., 2019). Given that laptops are relatively new, older adults refer to their novelty when they anthropomorphize them, thus assigning openness and stimulation to them.

Implications

There could be various practical implications for the description of the symbolic meaning of objects with the use of the value circumflex, as shown before (Allen, 2002; Allen et al., 2008; Shepherd et al., 2015; Torelli et al., 2012; Voorn et al., 2018). For example, when asked to make a decision, people prefer brands that match the values that they endorse. However, our findings show no correlation between personal values and the values that people attribute to devices. These results provide partial evidence against the “value-congruency” hypothesis, according to which the similarity between personal values and one’s perceptions of brand values influences purchasing preferences (e.g., Allen, 2002; Awad & Youn, 2018; Malhotra, 1988; Shepherd et al., 2015; Zhang & Bloemer, 2008).

Another implication relates to marketing endeavors. The current study suggests that it is not enough to consider personal values, and that advertisers should consider object values in and of themselves. Furthermore, generating an object’s symbolic profile with the full set of values may better address marketing efforts. Thus, it is possible that each specific symbolic meaning of an object—device or brand—calls for its own tailor-made corresponding advertising. However, as shown before (Torelli et al., 2012; Shepherd et al., 2015), differences between cultures and social groups should be taken into account. The age differences that emerged in Study 2 emphasize the need to address the relevant social group. We maintain that although personal values do not predict object values, social groups share object values and these affect personal attribution of values.

It should be noted that devices, or objects in general, do not equal brands. Although the devices that we examined refer to the category of objects (i.e., smartphones), brands are more concrete (e.g., Samsung or iPhone). Thus, brands may carry not only abstract values but also emotional meanings. We suggest that attributing values to objects may require more effort than attributing values to brands. However, the present studies demonstrate that such an anthropomorphizing process is possible, even for non-concrete, imagined objects (for similar results see Caspi et al., 2022). Future research may test ease of anthropomorphizing for concrete objects relative to more abstract entities.

In summary, we show that people consistently anthropomorphize devices by attributing values to them; that these values resemble the value circumflex that had been previously defined for personal values; that personal values do not predict object values; that anthropomorphizing electronic device does not depend on frequency or difficulty of use; and that object values differ across age groups. We suggest that Schwartz’s (1992) Theory of Basic Human Values can be extended to the measurement of the values that people attribute to objects, and that people use values to anthropomorphize objects.

Supplemental Material

Supplemental Material - Attributing Values to Devices

Supplementary Material for Attributing Values to Devices by Avner Caspi, Shir Etgar, and Gitit Kavé in Social Science Computer Review.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Avner Caspi

Shir Etgar

Gitit Kavé

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Avner Caspi is a senior lecturer at the Education & Psychology department, the Open University of Israel. His major research interests and publications focus on personal, interpersonal and social aspects of human communication with and by technologies.

Shir Etgar is a post-doctoral scholar at the Couples Lab in the Department of Psychology at Columbia University. Her research mainly focused on the impact of new technologies and new media on the different ways in which individuals construct and interpret their online and offline environments, with the goal of improving individuals' social and cognitive abilities in the current digital era.

Gitit Kavé is a professor at the Department of Education and Psychology, The Open University. She is also a Clinical Neuropsychologist. She studies language and aging, as well as neuropsychological assessment, with a focus on word retrieval and dementia.

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