*Corresponding Author 
 
 
 
 
 
 
EXTENDING TAM FOR INFORMATION SYSTEMS TO 
ACCEPTANCE RESEARCH/MODEL OF CONSUMER 
GOODS (CGAM): A THEORETICAL APPROACH 
 
 
Katrin Förster* 
Zeppelin University, 
Germany 
k.foerster@zeppelin-university.net 
Wolfgang H. Schulz 
Zeppelin University, 
Germany 
wolfgang.schulz@zu.de  
 
 
Abstract 
 
The aim is to broaden the Technology Acceptance Model (TAM) application 
to consumer goods and to conceptualise the Consumer Good Acceptance 
Model (CGAM) based on TAM while keeping its core elements and 
relationships as they were initially presented. Therefore, structural elements 
of the TAM were reviewed, critical statements of the usage of TAM and how 
it was constructed were considered to extend the TAM while eliminating 
flaws and creating value for manufacturers. The conceptualised CGAM is 
theoretically well supported. The framework upon which the TAM is created 
allows to adjust it to any acceptance process no matter which good it is. On 
condition that the definitions of the core elements of the TAM are adjusted, 
and relevant external factors for the consumer good are introduced. This 
study provides a new approach to widen the application area of the TAM by 
extending it to the acceptance of consumer goods. 
  
Key Words 
 
Technology Acceptance Model; consumer economics; consumer behaviour; 
consumer acceptance; behavioural intention. 
 
 
 
 
 
 
 

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INTRODUCTION 
 
The TAM is a widely applied model for technology-related acceptance 
research. It is an essential tool for researchers since acceptance can be 
seen as an influential variable for the successful introduction of new 
technology or products, as well as for its intended use (van der Laan, Heino 
& De Waard, 1997). Considering that people are “fundamentally decision 
makers” (Saaty, 2008, p.83) signifies that every action is eventually the result 
of a decision, consciously or unconsciously made (Nelson, 1970; Saaty, 
2008). Consequently, every consumption decision is linked to an evaluative 
process that finally leads to a decision and thus to accepting and using 
chosen services or products (Adell, 2009; Bettmann, Johnson & Payne, 
1991). This implies that acceptance is not only relevant in the context of 
technological products or IS but also for any consumer good designed to be 
bought by people.  
The phenomenon of acceptance has therefore not only been an important 
topic in the field of IS and technology research (Benbasat & Barki, 2007) but 
also for consumer research and marketing studies (Gefen, Karahanna & 
Straub, 2003; Kollat, Engel & Blackwell, 1970; Pikkarainen et al., 2004; Siró 
et al., 2008). Contemplating the nature of decision making and the part this 
process plays in any behaviour, it is clear that the decision to use or buy 
something can ultimately lead to not only accepting a technology, but also a 
product or service. Therefore, relating it to the TAM and the fact that TAM 
has been used to study consumer acceptance in various settings (McCoy, 
Galletta & King, 2007; Roy, 2017; Childers, Carr, Peck & Carson, 2001) and 
is well supported and accepted, it is logical to presume its successful 
extension to non-technological consumer goods. Thus adjusting the TAM 
would allow to forecast the acceptance of goods while assessing the 
influence of culture, social influence and product characteristics. 
This presumption proved to be accurate, based on the TAM's theoretical 
conceptualisation, showing a modified TAM, now called CGAM, is applicable 
for consumer goods. Consequently, the attempt to adapt the TAM for 
consumer goods is not only reasonable but also achievable and creates 
value for manufacturers. 
 
 
WHAT IS ACCEPTANCE? 
 
A common element in acceptance research is that there is no coherent and 
generally accepted definition of the term or how to measure acceptance 
(Lucke, 1995; Quiring, 2006). However, a consensus exists about the 
common and scientific use of the term, where its synonyms are “approval”, 
“diffusion”, and especially “adoption” (Lucke, 1995; Williams et al., 2009). A 
distinction between “acceptance” and “adoption” was made by Kollmann 
(2004) and by Renaud and Van Biljon (2008), describing “adoption” more 
like a process, and “acceptance” more like an attitude towards a technology 
playing an important role after the actual purchase. Considering the work of 
Adell (2009) and Kollmann (1998), acceptance is logically seen as a process, 

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and “adoption” and “acceptance” are used interchangeably. Especially when 
focusing on the acceptance of a consumer good, Adell’s (2009, p. 27) fourth 
criterion for acceptance appears to fit well applying it to consumer goods, 
namely: “This definition of acceptance aims for a behavioral change and may 
be seen as being based on the earlier categories, in that the will to use a 
system is based on drivers’ assessment of the usefulness of the system (as 
in category 2) as well as all other attitudes to the system and its effects (as 
in category 3).“ This acknowledges that a product cannot be accepted or 
declined without the consumer being aware of it, which presupposes that the 
decision to purchase, or at least sample, the product has already been made. 
This conception is coherent with the work of Adell (2009) and Kollmann 
(1998, 2004). 
Additionally, researchers agree on one common factor in the acceptance 
process: that an individual’s judgment affects acceptance and acceptability. 
Regan et al. (2002, p. 10) states that “While everyone seems to know what 
acceptability is, and all agree that acceptability is important, there is no 
consistency across studies as to what ’acceptability’ is and how to measure 
it.” 
Considering the statement by Regan et al. (2002), it becomes apparent 
that increasing knowledge about how and by what mechanism the 
acceptance of consumer goods is influenced, will not only help to improve 
acceptance but also provide valuable information for manufacturers as to 
how a product should be modified, and positioned in the market. Additionally, 
more profound knowledge may be gained of the nature of “acceptability” and 
how it could best be measured. 
 
 
TAM: WHY IT SHOULD BE USED TO PREDICT CONSUMER 
ACCEPTANCE OF NON-TECHNOLOGICAL CONSUMER GOODS 
 
The TAM developed by Venkatesh and Bala (2008), henceforth named TAM 
III, is an extension of the TAM originally devised by Davis (1989), and further 
developed as a TAM progression by Venkatesh and Davis (2000), herein 
referred to as TAM II. The original TAM was not only extended over the years 
through progressive research but also adapted to incorporate critical 
comment by Adams, Nelson and Todd (1992) and Davis and Venkatesh 
(1996). 
Davis (1989) designed the original TAM to explain and predict the 
behaviour of technology users. Further, he aimed to detect factors that 
influenced the acceptance or rejection process. In order to do so, Davis used 
the Theory of Reasoned Action (TRA) to connect the five factors: system 
design features, perceived usefulness (PU), perceived ease of use (PEOU), 
attitude toward using and the actual system use (Davis, 1989; Davis, 
Bagozzi & Warshaw, 1989). However, the model’s core elements PU and 
PEOU are influenced by external variables (Arnold & Klee, 2016). These two 
factors are relevant in order to control the beliefs users have about a system. 
The model can also be used to foreshadow the behavioural intentions of 
prospective users and their actual system use (Davis, 1989; Davis, Bagozzi 

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& Warshaw, 1989). TAM was criticised as a subjective norm was not 
included as an influencing factor (Arnold & Klee, 2016). This deficiency was 
eliminated by Venkatesh and Davis in 2000 when they aimed for 
explanations of PU and usage intentions based on social influence and 
cognitive instrumental processes. Subjective norm, voluntariness and 
image, define the social influence processes, implying “the ways other 
people affect one’s beliefs, feelings, and behaviour” (Mason, Conrey & 
Smith, 2007, p. 279; Venkatesh & Davis, 2000, p. 187). Cognitive 
instrumental processes are defined by job relevance, output quality, 
demonstrable results and perceived ease of use. By specifying the external 
variables influencing PU and behavioural intentions, Venkatesh and Davis 
(2000) created the TAM II. 
Further development of TAM II towards the formulation of TAM III by 
Venkatesh and Bala (2008) is based on the Venkatesh (2000) model of 
determinants affecting PEOU. Venkatesh subsequently addressed the 
criticism that there was a lack of understanding about the determinants 
influencing one of the key drivers of acceptance (Taylor & Todd, 1995). The 
determinants can be divided into two categories: anchor and adjustment 
variables. The anchor variables, namely computer self-efficacy, perceptions 
of external control, computer anxiety and computer playfulness, are based 
on initial judgment and will be adjusted over time with the accumulation of 
experience. The evolution of the original TAM towards a more 
comprehensive and precise TAM III reflects the four components PU, PEOU, 
behavioural intention and use behaviour (from TAM I), and the moderating 
variable, voluntariness, and the variable, experience influenced by time 
(introduced in TAM II), all being considered core elements of the model. 
Additionally, the determinants of PU and PEOU are invariably adjusted 
whenever the product of interest changes (Pikkarainen et al., 2004; 
Venkatesh & Bala, 2008). 
 
Limitations as Advocates 
 
That aside, the focus of this conceptual research approach is on TAM III, as 
the most advanced version of the TAM. Besides the already discussed 
limitations, which Venkatesh, Davis and Bala have eliminated, current 
research lists the following aspects as critical. 
Despite being a vastly accepted and widely used source for researchers 
TAM has been subject to criticism for leading research in new directions 
without fully understanding its antecedents (Benbasat & Barki, 2007). 
Despite this discussion being of great relevance in general, it is believed 
subordinate to the interests of this particular research at the present time. 
This belief arises because it is not the structure that is considered weak, but 
the restricted scope of research about its elements. Goodhue (2007) argues 
that TAM only reveals the factors influencing technology usage without 
capturing how technology affects user performance. Aiming to extend TAM 
to consumer goods, the effects on user performance are somewhat 
irrelevant, as the purpose of consumer goods is to satisfy needs, considering 
the four categories of consumer goods classification (Bucklin, 1963; 

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Holbrook & Howard, 1977; Holton, 1958; Luck, 1959), and focuses less on 
enhancing performances.  
Price and cost have not been addressed in TAM or its extension, which 
is not necessarily critical as the TAM was mainly applied in workplace 
settings, where price and cost are more relevant for the firm. However, in the 
market place for consumer goods, price and costs are of great relevance as 
they can have an immense impact on the decision to accept and consume a 
good (Brown & Venkatesh, 2005; Coulter & Coulter, 2007; Lunceford, 2009). 
This suggests the relative unimportance of time and effort as sole drivers for 
the acceptance process (Venkatesh, Thong and Xu, 2012). 
Additionally, voluntariness as a moderating variable is a theoretically well-
supported TAM element, including the individual utilization of innovation. A 
voluntary usage setting of Information Technology (IT) usage in a working 
environment is an arguable inclusion as such decisions are mostly based on 
top-down directives. Again, this “weakness” in TAM can be utilized to support 
its adaptation for consumer goods because in that context, consumers 
usually do have a choice whether or not to purchase and use a product. 
Consequently, this reservation does not apply to TAM in a consumer good 
setting, presuming that usage or purchase behaviour is voluntary. In contrast 
to voluntariness, social influencing factors that might not have a significant 
impact in the working environment, are of greater relevance for consumer 
good acceptance (Ang, Ramayah & Amin, 2015; Malatji, van Eck & Zuva, 
2020; Shan & King, 2015). 
 
Theoretical Backup 
 
So far, considering why TAM should be used to predict consumer 
acceptance of non-technological consumer goods, has been partially 
answered by using criticism of the model as advocacy for its extension 
towards consumer goods.  
Regarding the theoretical background of TAM there are other aspects 
supporting the extension.  
The initial TAM by Davis (1989) was built upon two theories that originated 
in the field of social psychology, the TRA by Ajzen and Fishbein (1980) and 
the Theory of Planned Behaviour (TPB) by Ajzen (1991). TRA is based on 
the precept that humans normally behave in a reasonable way, implying that 
information, both explicit and implicit, are considered. Similar to the process 
of acceptance, the theory is based on a causal chain; thus, different factors 
lead to specific behaviour. The enhancement by perceived behavioural 
control and consequently the development of the TPB allows, in contrast to 
the TRA, the inclusion of all behaviour – under full volitional control or not. 
TPB is the more suitable model to understand and predict human behaviour 
as the performance of a behaviour is more likely when its evaluation is 
positive, social pressure to perform the behaviour occurs, and people think 
they can do so or have the opportunity to do so (Ajzen, 2005). Given the 
origin of TAM, a modification of the TAM is a reasonable objective when 
creating an applicable model for the consumer goods market. This can be 
based on the relation of those theories with acceptance and consumer 

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behaviour. Accordingly, it can be assumed that the TAM would be applicable 
in testing the acceptance of any consumer good.  
Literature research showed various applications and enhancements of 
TAM, focusing on different IS and applying new external variables (Lee, 
Kozar & Larsen, 2003). Additionally, research has taken TAM out of the 
working environment context to successfully apply it to non-organizational 
settings (Agarwal & Karahanna, 2000; Davis, Bagozzi & Warshaw, 1989, 
1992; Mathieson, 1991; Szajna, 1994), supporting the research attempt to 
open up TAM for consumer goods.  
Considering that new processes and constructs were included in closing 
knowledge gaps regarding human decision-making processes and 
behaviour, the TAM has a profound theoretical foundation. This makes it 
transferable to most consumer behaviour-related research, as the model 
well covers human behaviour and decision-making processes. Thus, 
changing its application area from technology applications to consumer 
goods, in general, is theoretically well supported. Moreover, as discussed 
above, several points of criticism are advantageous for the extension of TAM 
into consumer goods. This research hypothesises that an extension of TAM 
for consumer goods is possible while assuming all effects are unaltered. 
Despite having argued that an extension of TAM for consumer goods is 
possible, the question of need and contribution remains. Several models are 
well-established in the field of consumer behaviour and marketing research. 
Table 1 shows some of these models, including their deficiencies and why 
the conceptualized CGAM is the better alternative. Additionally, it can be 
pointed out that some of these models are considered ‘grand models’ due to 
their large scope (Kassarjian 1982). Considerable complexity alone supports 
the case of transforming TAM into CGAM, as the aim is not only to extend 
TAM but to offer based on a well-tested model, a new model that allows easy 
adaptation even for manufacturers. Focusing the extension of the TAM on 
the external factors that influence PU and PEOU enables any company or 
product development department to adjust the model to their product of 
interest at a low expense rate. 
 
Table 1: Benefits of CGAM over existing models 
 
Model Focus Deficiencies Benefits of CGAM 
Engel-
Blackwell-
Kollat-
Model 
(1979) 
“1. To highlight more 
clearly the 
interrelationships 
between stages in the 
decision process and 
the various endogenous 
and exogenous 
variables. 
2. To clarify the 
relationship between 
attitude and behavior to 
reflect the contribution 
of Fishbein extended 
model. Beliefs and 
Information and 
experience are 
characterised as an 
important phase upon 
which several of the 
decision process stages 
of problem recognition 
are based. Additionally, 
the scope of application 
is unclear. (Rau & 
Samiee, 1981) 
The linear structure does 
not represent the buyer 
decision-making, as 
Experience is not a 
primary 
requirement; this is 
particularly 
valuable 
concerning 
innovative 
products. Thus, the 
CGAM enables 
acceptance to be 
tested even before 
the market launch, 
without the 
consumer having 

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intentions are 
introduced as explicit 
variables for the first 
time as in normative 
compliance. 
3. To define variables 
with greater precision 
and to specify functional 
relationships to permit 
empirical testing" 
(Engel, Blackwell, Kollat 
1979).  
those elements might not 
occur in that specific 
order or even 
concuurently (Bringer 
&Lutz, 1986; Phillips & 
Bradshaw, 1992). 
any experience 
with the product. 
Howard-
Sheth 
Model 
(1969) 
Explains consumer’s 
brand choice behaviour 
over a period of time 
based on stimulus-
response (Howard & 
Sheth, 1969). 
Complex, especially for 
routine purchases. 
Additionally, consumer’s 
do not follow the 
complete path of the 
model (Olshavsky & 
Granbois, 1979). 
CGAM does not 
specially require 
the consumer to 
follow a specific 
buying or decision 
process behaviour. 
Further, it is simple 
enough to be 
applied for all kind 
of goods, with high 
or low engagement 
rate. 
Nicosia 
Model 
(1966) 
Explains consumer 
behaviour by creating a 
link between the 
organization and the 
consumer (Nicosia, 
1966). 
Assumes the presence of 
predispositions 
influenced by firm or 
brand. Additionally, it is 
rather difficult to find a 
distinct focus, as 
Nicosia’s attempt to point 
out a focus is all-
encompassing. 
Additionally, it is from a 
marketer’s perspective 
and not a consumer’s. 
Validity of the suggested 
relationships is not 
empirically supported. 
(Nicosia, 1966; Rau & 
Samiee, 1981; Tuck 
1976) 
CGAM allows to 
test consumer 
acceptance for 
products that are 
new to the market 
or not specifically 
connected to a 
specific firm or 
brand. 
The theory 
of planned 
behavior 
(TPB) 
(Ajzen, 
1991) 
“The theory of planned 
behavior (TPB) has 
been used successfully 
to explain and predict 
behavior in a multitude 
of behavioral domains.” 
(Ajzen, 2020, p. 341) 
Non in this context. Was 
used as foundation for 
TAM. 
As Ajzen stated 
1991 the TPB is 
open for further 
adjustment and 
introduction of new 
predictors, thus 
considering how 
Davis (1989) did 
this a further 
adjustment while 
considering the 
current variables is 
in principle 
possible (Ajzen, 
1991). 
Source: Own survey. 

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Summarising, the benefit of CGAM is that it allows an early-stage 
acceptance determination while also generating knowledge about the factors 
influencing the level of acceptance. Additionally, CGAM does not focus on 
brands or firms or implies knowledge about a firm or brand, nor does it 
presume experience. Therefore, CGAM is a more practice- and 
manufacturer-oriented model. 
 
 
CONCEPTUALISATION OF CGAM 
 
As stated above, Pikkarainen et al. (2004) and Venkatesh and Bala (2008) 
pointed out that the external variables determining PU and PEOU will need 
adjustment whenever the product of interest changes. These determinants 
correspondingly form the starting point for the modification process. They 
can be divided into four categories: social influence processes, cognitive 
instrumental processes (Venkatesh & Davis, 2000), as well as anchor, and 
adjustment, variables (Venkatesh & Bala, 2008).  
In contrast to other approaches extending the TAM beyond IS, where 
variables were incorporated depending on the specifics of the context within 
which the study’s focus was situated (Hsu & Lu, 2004), extending TAM for 
consumer goods calls for a more radical approach. As it is not sufficient to 
just add more variables or exchange a few, the complete set of variables 
determining PU and PEOU will need to be adjusted so that the 
characteristics of the good of interest can be met, consequently requiring a 
departure from the main body of TAM III as Venkatesh and Bala (2008) 
conceived it. This approach was also suggested by Benbasat and Barki 
(2007). In order to extend TAM, the core elements will remain as they were, 
although the definitions of PU and PEOU need also to be adjusted. Some of 
the variables influencing PU and PEOU will be adopted from TAM III, with 
consequent changes in the definitions required. All newly integrated 
variables are, on the one hand, defined based on the literature, while 
incorporating the finding from Förster (2017), or the definitions are, on the 
other hand, based on what the construct aims to address in the case of 
consumer good acceptance. 
Admittedly, this approach is rather general, as no classification for what 
kind of consumer good this model will be conceptualised is given. There are 
two types of consumer goods, which are further segmented into three 
groups. The first type concerns frequency and duration, differentiating 
between durable, semidurable, and non-durable goods (Graber-Kräuter, 
2018). The buying decision process characterises the second type, which 
includes convenience, shopping, and speciality goods. For more detailed 
definitions, see Holton (1958). Since this paper is based on Förster (2017), 
the focus is on convenience goods. However, the goal is to develop a 
generally applicable CGAM. 
PU was defined as “the degree to which a person believes that using a 
particular system would enhance his or her job performance” (Davis, 1989, 
p. 320). Since systems are no longer the focus, the perception of what 
constitutes PU, and its addresses, must be modified. A more social 

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influence-based definition was proposed by Förster (2017, p. 35): “PU is the 
degree to which a person believes that consuming a particular product would 
increase their social status or contribute to the image they are trying to 
communicate to the outside world” consistent with their lifestyle and needs 
(as subsequently qualified). In order to adjust the definition for PEOU to be 
more product-oriented, Förster (2017, p. 35) defined PEOU as follows: 
“PEOU is the degree to which a person believes that consuming or buying a 
product would be free of effort” while also considering the investment-to-
benefit-ratio (as subsequently qualified). Both definitions are addressing the 
shift towards products while failing to include all categories of determinants. 
Before introducing new definitions for PU and PEOU, it is indispensable to 
consider the definitions of the four variable categories influencing PU and 
PEOU.  
The concepts influencing PU are social influence processes, which imply 
“the ways other people affect one’s beliefs, feelings, and behavior” (Mason, 
Conrey & Smith, 2007, p. 279; Venkatesh & Davis, 2000, p. 187) and 
cognitive instrumental processes. The latter focuses on the linkage of higher-
level goals to specific actions which are essential for goal achievement 
(Venkatesh, 2000). Förster (2017, p. 39) introduced a new description of 
cognitive instrument processes: “consumers form judgments about PU in 
parts based on cognitively comparing what a product is capable of in terms 
of representing what they want to communicate to the outside world”. In 
addition, the variables influencing PEOU are anchor and adjustment 
variables. Anchors are general information upon which individuals can rely 
on in the absence of specific knowledge. Besides, anchoring information is 
often unconsciously included in the decision-making process. Adjustment 
variables relate to beliefs based on actual interaction with a system or 
product (Venkatesh, 2000).  
The two moderating variables, experience and voluntariness, are also 
seen as core elements of TAM. Venkatesh and Davis (2000) implied that 
experience diminishes the effect social influence has on PU and usage 
intention over time. Voluntariness is defined as “the extent to which potential 
adopters perceive the adoption decision to be nonmandated” (Agarwal & 
Prasad, 1997, p. 564). This would differentiate the mandatory and voluntary 
usage settings, as the contingency of mandatory usage setting impacts the 
compliance effect of subjective norms on the intention to use. Additionally, 
the effect of a subjective norm on intention to use is strengthened by the 
compliance effect, increasing as an individual’s need to fulfil a social actor’s 
expectations. The social actor is considered as an executor of rewards and 
punishment dependent on the implementation and non-implementation of 
the relevant behaviour (French & Raven, 1959; Venkatesh & Davis, 2000). 
Figure 1 presents the framework upon which the CGAM will be 
conceptualised while considering all TAM III’s underlying assumptions as 
correct. 

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Figure 1: TAM III as framework for conceptualisation
Source: Own illustration based on Venkatesh & Bala (2008). 
 
Considering the previously discussed definitions of the variable 
categories influencing PU and PEOU, determinates can be selected based 
on consumer good characteristics and their general validity. General validity 
is important here in so far as the determinants should be applicable for 
consumer goods in general.  
 
Variables of the construct of social influence processes  
 
Starting with variables of the construct of social influence processes, 
subjective norm and image are two determinates which are adapted from 
TAM. Subjective norm was defined as a “person’s perception that most 
people who are important to him think he should or should not perform the 
behaviour in question” (Fishbein & Ajzen, 1975, p. 302) and has direct 
influence on PU and behavioural intention, as well as image. Research by 
Stafford (1966) supports the integration of the variable subjective norm, as 
his research showed that adaptation of brand choices is often influenced by 
the leader of one social group since he or she influences its group members. 
Image also has a direct effect on PU and is a significant factor when it 
comes to communicating one’s own image to the outside world. The 
transferred image can be linked to the self-concept of a person, which is 
described as “beliefs a person holds about their attributes, and how they 
evaluate these qualities” (Solomon et al., 2010, p. 144). In the context of 
social interaction, the image that is communicated to others evokes a higher 
concern “about the social appropriateness of products and consumption 
activities” (Solomon et al., 2010, p. 149). In the context of consumer goods, 
there are two relevant definitions regarding image. First, image as "the 
degree to which use of an innovation is perceived to enhance one’s image 
or status in one’s social system" (Moore & Benbasat, 1991, p. 195); this is 
consistent with TAM II. Second, Keller defined image in the context of brands 
as “perceptions about a brand as reflected by the brand associations held in 
memory” (Keller, 1993, p. 2). Taking into account that TAM originated from 
Voluntariness
Constructs social 
influence processes
Constructs cognitive 
instrumental processes
Perceived Ease of 
Use
Behavioural 
Intention
Use Behaviour
Technology Acceptance Model
Perceived Usefulness
Experience
Adjustment variables
Anchor variables

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theories in the field of social psychology, it is of great relevance that a 
person’s image can be related to a product image, including variables 
addressing these topics. 
A newly integrated variable in the context of social influence processes is 
social conformity; addressing a person’s desire to fit in, while avoiding 
negative attention (Jahoda, 1959). Cialdini and Goldstein (2004, p. 606) 
referred to conformity as “the act of changing one’s behavior to match the 
responses of others”. Considering social conformity, especially in the context 
of consumer goods, is important. As was pointed out by McCracken (1986), 
consumer goods are used to extract cultural meaning, since cultural 
meaning is projected from a culturally constituted world onto consumer 
goods. Furthermore, through goods, cultural meaning is made visible for the 
individual as well as conveying an inherent concreteness that individuals 
would not otherwise have (McCracken, 1986). Attitudes and expectations 
subconsciously received from others also influence one’s own behaviour. 
Thus, considering social influence for the consumer goods' acceptance 
process is a necessity (Venkatesh & Morris, 2000). 
Considering all proposed variables for the construct of social influence 
processes, their relevance is supported by a statement made by McCracken 
(1986, p. 73): “goods allow individuals to discriminate visually among 
culturally specified categories by encoding these categories in the form of a 
set of material distinctions”.  
All three variables directly affect PU, while the subjective norm is also 
moderated by experience and affects behavioural intention. The latter 
relation is not only moderated by experience but also by voluntariness. 
 
Variables of the construct of cognitive instrumental processes 
 
Moving on to the construct of cognitive instrumental processes, two variables 
can be used irrespective of the consumer good of interest. The first variable 
is lifestyle and the second is perceived quality/brand status. “Lifestyle is 
described as a certain type of behavior, or preference for a certain type of 
behavior, in which consumption plays an important role” (Sijtsema et al., 
2002, p. 572). Perceived quality, as well as brand status, can influence the 
market success of a product (Richardson, Dick & Jain, 1994). Kolter (2000, 
p. 3) described quality as “the totality of features and characteristics of a 
product or service that bear on its ability to satisfy stated or implied needs”. 
Additionally, O’Cass & Choy (2008, p. 342) hold that “Brand status refers to 
consumer’s perceptions of quality, prestige, price of a brand and its capability 
to act as a status or success symbol”. Combining these two aspects enables 
a consideration of the quality-related characteristics of the consumer goods 
and how these are perceived by the consumer, as well as delving more 
deeply into interactions between the two variables i.e., how the perception 
of brand status influences perceived quality, and vice versa.  
A third variable for measuring the construct of cognitive instrumental 
processes could be the trend factor, although it might not have significant 
relevance for some consumer goods, as for example, convenience goods as 
described by Holton (1958, p. 53): “convenience goods are those goods for 

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which the probable gain from making price and quality comparison among 
alternative sellers is thought to be small relative to consumer’s appraisal of 
searching costs in terms of time, money and effort”. It can also be assumed 
that for those goods, the benefit of considering the tendency to change would 
also be small compared to the effort needed to assess the trend factor of 
that good.  
These three variables directly affect PU without being moderated by 
experience or voluntariness.  
 
Anchor variables 
 
Moving on to the determinants of PEOU, there are two variables that can be 
proposed as generalised anchors while recognising that the selection is 
based on constructs addressing purchase-situation related issues. First is 
availability, relating to how easily a product can be accessed, taking into 
account its present for the consumer and the frequency, intensity and 
visibility of product offerings. Second is price, which “refers to the cost or 
sacrifice exchanged for the promised benefits” (Grier & Bryant, 2005, p. 323), 
and influences every consumption decision. However, the importance of 
these varies depending on the consumer good in question, as research has 
shown price sensitivity varies among products and consumption settings 
(Wakefield & Inman, 2003). 
A third option, which might not be as generalisable as the previous two is 
handling. In this context, issues regarding the general handling of a product 
can be included, e.g., the effort needed to consume a product or prepare it 
for consumption. Depending on the product in question, other aspects such 
as the way the product is offered, the temperature of consumption and 
offerings, can also be of relevance. Additionally, aspects concerning 
changes in those characterises can determine whether change can improve 
acceptance by simplifying handling. The variables effecting PEOU are all 
moderated by experience. 
 
Adjustment variables 
 
The adjustment variables presented by Venkatesh and Bala (2008) need to 
be replaced, as previously explained, by adjustment variables that can be 
used to address more specific consumer good characteristics. Therefore, 
additional value, sensory experience and indulgence are proposed. 
Additional value refers to “the tangible and concrete attributes that a 
consumer may directly experience when using or consuming the product” 
(Lai, 1995, p. 383), hedonic benefits that are “acquired from a product's 
capacity to meet a need of enjoyment, fun, pleasure, or distraction from work 
or anxiety” (Lai, 1995, p. 384), as well as holistic benefits, which are 
“perceptual benefit(s) acquired from the complementarity, coherence, 
compatibility, and consistency in a product constellation as a whole” (Lai, 
1995, p. 384). 
 

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62 
Considering that consumption is not only influenced by external factors 
such as availability, price and handling, there are more complex enjoyment-
focused adjustment variables such as sensory experience, that needs to be 
considered. Sensory experience includes the perception of a product with all 
your senses, including visualising, smelling, hearing, touching and tasting 
(Smith, 2013). Certainly, sensory characteristics concerning taste, texture 
and appearance can influence the consumption decision (Clark, 1998). 
Therefore, sensory-experience is an adjustment variable, just as hands-on 
experience is necessary to evaluate these aspects of a product. With regard 
to criticism (Venkatesh, 2000) observes that TAM does not give valuable 
information to manufacturers on how to guide development, including the 
construct that sensory experience helps overcoming this deficiency. 
The third adjustment variable is indulgence which can be referred to as 
enjoying life and having fun (Hofstede, 2011). However, indulgence is also 
often associated with adverse outcomes or negative feelings, especially 
when they result from impulsive behaviour (Ramanathan & Williams, 2007).  
 
All three variables are moderated by experience while affecting PEOU. 
 
Figure 2: Consumer Goods Acceptance Model 
 
Source: Own illustration based on Venkatesh & Bala (2008). 
 
 
DISCUSSION & CONCLUSION 
 
This paper presents a conceptualised model, based on TAM, which should 
help to measure acceptance of a product and by what process the 
acceptance of the product is effected. Additionally, it is hoped to encourage 
Voluntariness
Social Conformity
Image & Self-concept
Lifestyle
Perceived Quality / 
Brand Status
Subjective Norm
Availability
Handling
Price
Additional Value
Sensory Experience
Perceived Ease of 
Use
Behavioural 
Intention
Use Behaviour
Technology Acceptance Model
constructs social influence processes
constructs cognitive instrumental processes
relations adapted from TAM III
Anchor
Adjustment
Perceived Usefulness
Experience
Indulgence

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63 
fellow researchers to consider this approach and take the idea of TAM to the 
next level by using the proposed CGAM. 
The revision of the theoretical concepts of the TAM showed that the newly 
conceptualised CGAM has great potential, proving to be a valid extension of 
the TAM to consumer goods in practice. First, acceptance is a core element 
of the TAM and a significant variable of every human decision-making 
process (McCracken, 1986). Behaviour can be influenced, as well as led by 
acceptability, as the theoretical work by Adell (2009) and Kollmann (1998; 
2004) showed. Understanding the concept of acceptance and how reaching 
the state of accepting something is a crucial process when extending the 
TAM towards consumer goods. Second, human behavioural patterns and 
decision-making processes are essential for the TAM, given that its structure 
originates from the TRA and TPB (Ajzen, 1991; Ajzen & Fishbein, 1980). 
Those topics are also well covered in the CGAM. Third, TAM’s development 
process towards TAM III reflects considered research and ensures that 
known shortcomings and flaws have been dealt with (Venkatesh, 2000; 
Venkatesh & Bala, 2008), giving rise to a well-structured and supported 
model. Using the TAMs theoretical background and its structure to widen its 
application area to consumer goods in general, is also well supported, as the 
foundation is well-based on social psychological constructs, which are 
crucial to understanding and predicting human behaviour (McCracken, 
1986). 
The presented model is accompanied by some blanks for which variables 
are proposed in this paper. However, whether these blanks for variables can 
be generalised or better left as blank spaces to fit the characteristics of the 
product of interest, still needs to be discussed. The presented model is a 
solid framework that can be used to study consumer acceptance for 
consumer goods while allowing researchers to readily adjust the model to 
their needs. Considering how often TAM has been modified to be applicable 
for different technology use settings (Lee, Kozar & Larsen, 2003; 
Taherdoost, 2018), leaving the blank spaces as presented in Figure 2 seems 
appropriate and more flexible, considering the vast diversity of consumer 
goods (Bucklin, 1963; Grabner-Kräuter, 2018). In Figure 2 the specifically 
listed variables are based on the outcomes of the literature research, and 
highly recommended for the acceptance assessment of consumer goods. 
The variables suggested for the blank spaces, such as trend factor or 
handling, need to be reassessed when applying the model to a specific type 
of consumer good.  
Since this study is based on Förster’s (2017) approach to extend the TAM 
by using tea-to-go as a proxy for consumer goods, more specifically 
convenience goods, this issue has not been further addressed. However, as 
mentioned above, there are several types of consumer goods. Hence future 
research should test the CGAM as it is presented while comparing its 
applicability, validity and reliability using different kinds of consumer goods. 
In addition to that, it would be possible to assess whether the presented 
CGAM is simple and thus generalisable or too specific and therefore not 
applicable for any type of consumer good. 

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64 
In the CGAM there are several variables focused on the factors 
influencing human behaviour, but also several variables which allow the 
representation of characteristics of consumer goods. While leaving the 
model with blank spaces might be seen as an easy way out, they strengthen 
the CGAM insofar as they allow specific inputs to correspond to the product 
being considered, thus allowing greater specificity and flexibility in use. 
Furthermore, these blanks create flexibility and easy application, which is 
crucial for manufacturers, who need to test possible acceptance of newly 
developed products in a quick and easy manner, that still generates valuable 
and reliable insights. 
In order to test the validity of the presented model it needs to be applied 
in a specific use situation. In a following paper the conceptualised CGAM will 
be applied using “tea-to-go” as an example for consumer goods. As 
discussed, applying the model to a specific consumer good might require the 
addition of some variables that relate to the specific characteristics of the 
consumer good. Additionally, some of the embedded variables may need to 
be reconsidered and tested for their applicability in the context of the “tea-to-
go” case. 
Despite having conceptualized a consumer good acceptance model 
without having specified the type of consumer good, the contribution is not 
totally limited. As there is still need for theoretical adjustment and further 
exploration, the presented CGAM has great value for the German tea-
industry, as they gave insights about challenging issues and key factors. 
Those have been greatly considered while developing the new variables for 
CGAM (Förster, 2017) and thus CGAM can be used to test the acceptance 
and it’s driver for tea-to-go.  
 
 
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