The Acceptance Behavior of New Media Entertainment Among Older Adults: Living Arrangement as a Mediator

Abstract

New media entertainment is currently being spotlighted by business practitioners and researchers. This article highlighted this issue of elder online users and explored the factors affecting their acceptance decisions in new media entertainment. Older adults prefer to status and value orientations, so their online acceptance of new media entertainment is significantly influenced by the perceived usefulness and social benefits. In addition, types of living arrangement significantly affect technology acceptance model of older adults.

Keywords

older adults new media entertainment technology acceptance model acceptance behavior living arrangement

Introduction

Due to the development of digital and information technologies, more new media entertainment products and services are available for media consumers of all ages (Dogruel, Joeckel, & Bowman, 2015). Adoption and acceptance of new media entertainment by young consumers have been studied profoundly (Atkin, 1985; Evans et al., 2014; Vorderer, Klimmt, & Ritterfeld, 2004), but the adoption of new media technologies is not a straightforward affair for elder people who are first confronted with these technologies in their later life stage (Czaja & Lee, 2007) and has been seldom discussed. Therefore, it is necessary to explore the adoption of new media entertainments among elderly customers for addressing potential markets.

Young consumers and older adults may use the Internet differently (Yang, 2013). It is generally believed that older adults are more likely to seek health information and products online, but more of them actually download videos and music, play games, and post blogs online (Ryu, Kim, & Lee, 2009). Moreover, when it comes to purchasing entertainments online, older adults of Chinese show distinct characteristics of online behaviors such as being more anxious about information technologies and being more self-satisfied during the purchase process (Venkatesh & Speier, 2000).

An increasing number of online entertainment providers are recognizing the importance of older customers as a potential market and thus designing or developing products and services specifically for older adults. However, the acceptance of new media entertainment by older people lags behind that by younger people. There are many reasons that older adults are excluded from new media entertainment including related financial constraints, health obstacles, and the lack of instructions and prior experiences. Younger customers have learned how to use a computer or online games at school or from friends, but this is often not the case for older adults, especially those who have not contacted Internet before (Barnard, Bradley, Hodgson, & Lloyd, 2013).

Although some relevant analyses have been conducted by various market researchers on older adults of online customers, most of these analyses are descriptive in nature and highly dependent on subjective judgment. It is necessary to perform more well-designed academic studies based on rigorous analytical models and sophisticated statistical methods in order to understand the online behaviors of older adults (Lian & Yen, 2014). Information technology acceptance theories such as the technology acceptance model (TAM), innovation diffusion theory, task technology fit, and the unified theory of acceptance and use of technology have been developed to analyze the factors that influence the acceptance of new information technologies and examine online customers’ behaviors (Gefen, Karahanna, & Straub, 2003). Yet, the studies in this field mainly focus on information and task-related acceptance of new technologies such as Internet usage, whereas social demands and living arrangement conditions of older adults are rarely considered in technology acceptance studies.

Many previous studies treated living arrangement as an important factors in TAM because of cultural and traditional traits of older adults (Barnard et al., 2013; Ma, Chan, & Chen, 2016; Sun & Zhang, 2006; T. Teo & Noyes, 2011; Venkatesh & Speier, 2000; Yang, 2013). For Chinese seniors, living arrangements are involved with children and family. Different types of living arrangements provide a different environment for older adults using new media entertainment. It is necessary to incorporate living arrangement-centered constructs, which can explain how TAM factors influence older adult customers on acceptance of new media entertainment, since these constructs are complementary to TAM. In particular, current theoretical frameworks are not enough for interpreting the impacts of some novel constructs, such as social benefits (SBs), trust, and perceived financial cost (PFC) on older customers. The study aims to enhance these frameworks by answering the following questions:

How do living arrangements affect TAM on acceptance of new media entertainment among older adults?

How do SBs affect TAM on acceptance of new media entertainment among older adults?

How do TAM and TAM2 explain the acceptance of new media entertainment among older adults?

This article is organized as follows. Section Theory Backgrounds and Hypotheses presents the theoretical framework and research model for the study. Section Methodology and Data Collection describes the methodologies used for research and data collection. Section Results presents the result analysis. Section Discussion and Conclusion delivers discussion, theoretical and practical implications. The final section gives the limitations and future research of this study.

Theory Backgrounds and Hypotheses

We first discussed our theoretical framework focusing on the TAM and TAM2. Second, we carved out the characteristics of living arrangement and new media entertainment technology. Third, we proposed the hypotheses to be tested in our study.

Research Following TAM and TAM2

To analyze factors influencing the acceptance of new technologies, information technology acceptance theories such as the TAM, innovation diffusion theory, task technology fit, and the unified theory of acceptance and use of technology have been adopted to examine online customers’ behaviors (Choi & Totten, 2012; Leong, Ooi, Chong, & Lin, 2013). TAM developed by Davis (1989) is widely accepted as it can be easily expanded to answer specific questions such as the acceptance of information technology. The core thoughts of TAM are the conceptualization of acceptance behaviors as a function of two individual beliefs: perceived usefulness (PU) and perceived ease of use (PEOU) (Davis, 1989). The fundamental goal of TAM is to analyze the influences of these influencing factors on the acceptance or rejection of information technologies.

Venkatesh and Davis (2000) proposed an extended model based on TAM and sought to identify more variables affecting the acceptance of new technologies beyond usefulness and ease of use, such as subjective norms (influence from others), task characteristics (e.g., purpose of the technology), type of information being sought, individual and cultural differences (e.g., gender), workplace environmental factors (e.g., voluntary system use) (Venkatesh & Speier, 2000), and social influence (Zhou, Zhang, & Zimmermann, 2013). The extended TAM, called TAM2, is widely accepted by researchers and practitioners and it focuses on theoretical factors spanning different aspects of social influence processes, perceived hedonism, and cognitive instrumental processes (Galan, Giraud, & Meyer-Waarden, 2013). TAM2 has been used to predict the intention to use in a wide variety of populations including students, online consumers, and older adults. For example, in order to predict elderly’ online behaviors, Ponte, Carvajal-Trujillo, and Escobar-Rodríguez (2015); T. Teo and Noyes (2011); Venkatesh and Speier (2000) used TAM and TAM2 to interpret the preferences of online old shoppers and found that perceptions of usefulness and ease of use, as well as trust, SB, and financial cost influenced online shopping behaviors of older adults. TAM and TAM2 have also been used by elderly related research works in digital services (Ryu et al., 2009). We believed that they could also be used to analyze the elderly’s adoption of new media entertainment.

Division of Living Arrangements

Many countries are in the midst of demographic aging (Walker & Maltby, 2012), especially Asian countries such as China, Korea, and Japan. Chinese elderly population is increasing rapidly, both in absolute numbers as well as the proportion of elderly population to total population as a result of the One-child Policy implemented in China since the 1970s (Zhang, 2013). With Chinese economic development and changing of family structures, a living arrangement of the elderly has become a serious social problem. Filial piety plays a particularly important role in Chinese culture. Children and other family members are thought to be indebted to the older generation and are obliged to assist in their physical, emotional, and material well-being (Zimmer, 2005). Traditionally, filial piety to parents of a family is developed in a system of coresidence with grown children. However, filial piety is challenged because Chinese families, like those in other countries, are experiencing great changes. For example, the substantial increase in one-person and one-couple-only households and the decrease in average household size decrease the capability of family support to older adults. More specifically, the proportion of the elderly who do not live together with children and the proportion of one-couple-only households in the elderly population increased considerably (Jiang & Gu, 2008). Therefore, multiple supporting sources of older adults developed with the lead of government, such as state pensions provided to employees in state economic sectors; support of childless elders through an income maintenance program; and the development of nursing homes for the aged in urban areas (Ryu et al., 2009). Generally, Chinese older adults, especially urban ones, can receive their filial piety through living with children as tradition, moving into nursing homes operated by government or enterprises, living with a spouse. According to Yi and Wang (2003), although the proportion of older adults living in nursing homes is increasing, older adults living with children, living with a spouse or living along are still the main choice of Chinese older adults and was estimated to account for 75% of all older adults.

To understand the effects of living arrangements on the acceptance of new media entertainments among older adults and facilitate the study, we divide all living arrangements of older adults into two groups: living with children and living without children (living with a spouse or others or living alone). Such division of research sample based on: (a) the main source of support to older adults in China is still the family, and any alternative sources are concentrated almost entirely among the smaller proportion living in urban areas (Zimmer, 2005); (b) older Chinese who living with children reported a significantly higher level of dependency on others in the instrumental activities of daily living, lower level of social support, and less self-rated financial adequacy and income (Lai, 2005); (c) Chinese elderly living with children depend less on information technology than other older adults because they have more chance to contact new media entertainment facilities and receive more direct help from younger family members.

Variables and Development of Hypotheses

Perceived usefulness

PU is the user’s belief on the influence of a technology on a certain goal. Specifically, in a shopping environment where a technology can be used to enhance the purchasing performance, PU may play a significant role in encouraging the adoption of new systems (Venkatesh & Speier, 2000). Previous empirical studies indicated the proposition that PU was the primary predictor of information technology acceptance (Barelka, Jeyaraj, & Walinski, 2013; Bhatti, 2015; Okazaki & Mendez, 2013). PU as the dominant variable of TAM is appropriate for research areas in acceptance of new media entertainment since it is based on computer technology. Therefore, it is justifiable to extend TAM to examine the acceptance of new media entertainment among elderly adults. Based on previous literature, the following hypotheses are developed:

Hypothesis 1 (H1): PU has a significant impact on the acceptance of new media entertainment among the elderly.

Hypothesis 1a (H1a): PU has more impact on the acceptance of new media entertainment among elderly living without children than those living with children.

Perceived ease of use

PEOU encourages intention to use because individuals who perceive themselves capable of carrying out an action are more likely to do so and individuals are more likely to pursue a course of action that they feel will deliver the greatest amount of gain for the least amount of efforts (Venkatesh & Speier, 2000). This construct is posited to influence behavioral intentions to use new media entertainment through two casual pathways: a direct effect as well as an indirect effect through PU (Bhatti, 2015). It is one of the major behavioral beliefs influencing users’ intention to technology acceptance in both TAM and TAM2. Previous literature indicated that older adults might feel more anxious when using Internet and accessory facilities, thus resulting in a lower level of PEOU (Ryu et al., 2009). It is found the ease of use is a key factor in determining how likely older adults were to adopt technologies (Gilly & Zeithaml, 1985). More studies also indicated that the PEOU was an important factor of TAM with older adults (Ponte et al., 2015; Venkatesh & Speier, 2000). Therefore, the conclusions are drawn as follows:

Hypothesis 2 (H2): PEOU has a significant impact on the acceptance of new media entertainment among the elderly.

Hypothesis 2a (H2a): PEOU has more impact on the acceptance of new media entertainment among elderly living without children than those living with children.

Perceived enjoyment

Perceived enjoyment (PE) is conceived as the extent to which the activity of using computers is perceived to be enjoyable in its own right, apart from any performance consequence that may be anticipated (Davis, Bagozzi, & Warshaw, 1992). In contrast to PU and PEOU, which are seen as extrinsic motivations, PE is regarded as an intrinsic motivation to use a technology (Venkatesh & Speier, 2000) and can be used to interpret the behavioral intention to use a technology (Van der Heijden, 2004). It has been confirmed that PE plays an important role in users’ technology acceptance and has great implications, especially for hedonic systems (Sun & Zhang, 2006). Suki and Suki (2011) indicated that an elderly could be more motivated to do or repeat an activity that was enjoyable. While PU and PEOU could theoretically, taking into account minor amendments, also be used to assess technology aimed at entertaining the user, there would be clear drawbacks from such a transition. However, the use of PE as an external variable in TAM or TAM2 is not commonly found in studies relating to technology acceptance in new media entertainments. However, we believe that when an older user can experience the enjoyment through the adoption of new media entertainment technology, their attitude toward acceptance will be positive. Therefore, we hypothesize that:

Hypothesis 3 (H3): PE has a significant impact on the acceptance of new media entertainment among the elderly.

Hypothesis 3a (H3a): PE has more impact on the acceptance of new media entertainment among elderly living without children than those living with children.

Trust

Trust refers to an individual’s belief or an expectation of others’ ethical behaviors under various influential factors such as subjective norms, risk, confidence, and security. TAM has been extensively used with an additional construct, trust, to find the acceptance and adoption of a new technology (Belanger, Hiller, & Smith, 2002). Building users’ trust in a technological environment with different channels such as the Internet, phone, TV, and mobile is a critical challenge for managers and researchers (Ponte et al., 2015). Previous studies indicated that trust was an essential factor in the entertainment market because it could decrease the operational costs considerably (Ponte et al., 2015; T. S. H. Teo & Liu, 2007) and increase the information sharing and purchase intention. Trust is also a positive predictor for other people’s interaction and behavior (Gefen et al., 2003). Trust also refers to a user’s belief in the security, dependability, and competence of a system that the user is working with, especially under the conditions of a risk. Without trust, users tend to have less interactions with the service and related parties (Chang, Shen, & Liu, 2016). Ryu et al. (2009) believed that older customers in e-commerce were more rational and more sensitive to risks than younger generations, but Rodgers (2003) believed that trust with emotion and convenience could predict dissatisfaction in online commerce and proposed a conceptual framework. The effects of different living arrangements on older customers’ perception in e-commerce had been discussed (Ryu et al., 2009; Zhang, 2013) and the effect of trust on the acceptance of a new technology was more significant among older adults, Therefore, the hypotheses are drawn as follows:

Hypothesis 4 (H4): Trust has a significant impact on the acceptance of new media entertainment among elderly.

Hypothesis 4a (H4a): Trust has more impact on the acceptance of new media entertainment among elderly living without children than those living with children.

eWOM

Word-of-mouth (WOM) is defined as the act of exchanging marketing information among consumers and plays an essential role in changing consumer attitudes and behaviors toward products and services (Buttle, 1998). With the emergence of the Internet, electronic WOM or online WOM (eWOM) has showed an important influence on customers’ behaviors (Doh & Hwang, 2009), which was usually taken as a symbol of service trustworthiness and quality. Hennig-Thurau, Gwinner, Walsh, and Gremler (2004) contributed that eWOM as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet.” Since eWOM is created and delivered by a more trustworthy source of information about products and brands than company generated persuasive messages, customers often rely on it when they search for information on which to base their purchase decisions.

Online customers can evaluate several explicit and implicit cues concerning the provider of a product or service to gradually build up PU and trust (Hajli, Lin, Featherman, & Wang, 2014). Among these cues, attribute eWOM and PEOU represent evaluations of direct experiences (Hazard & Singh, 2011; Singh & Sirdeshmukh, 2000). If favorably perceived, adverse selection and moral hazard concerns will be reduced and online customers will have more confidence in the provider; thus increasing their acceptance of new technologies (Chiou & Droge, 2006). Thus,

Hypothesis 5 (H5): eWOM has a significant impact on the acceptance of new media entertainment among elderly.

Hypothesis 5a (H5a): eWOM has more impact on the acceptance of new media entertainment among elderly living without children than those living with children.

Social benefit

D. J. Kim, Ferrin, and Rao (2009) indicated that SBs could increase the value and commitment among online customers, which included a sense of belonging, recognition, feelings of the team, friendship and even social support. SB improves the commitment perception of customers to service providers in e-commerce (Gao & Liu, 2014; Zheng, Zhu, & Lin, 2013) and has more influence on purchase intention in an e-commerce environment, especially in social commerce (Tsai & Yeh, 2010). SB has been regarded as an important factor to increase the acceptance of information technology since SNS developed quickly (Chu & Kim, 2011; Curty & Zhang, 2013; Taylor, Lewin, & Strutton, 2011). Social interaction afforded by online purchasing with friends or families is a prime motivator for consumer retail patronage which has received much attention from both scholars and practitioners (Bardhi, Rohm, & Sultan, 2010; Kiron, Palmer, Phillips, & Kruschwitz, 2012).

Older adults need social activities as young adults but they are hindered by living environment, health condition, and other factors (Luo, Zhang, & Gu, 2015). Therefore, finding a company, a sense of belonging, and social support during the acceptance of new media entertainment would inspire older adults’ behaviors. Morris, Venkatesh, and Ackerman (2005) found that older male customers were more influenced by instrumentality, whereas older female customers are more strongly influenced by social factors and environmental constraints. However, no significant gender difference in the determinants of technology use was reported. Fagenson (1993) indicated that the gender role had a strong sociopsychological basis and was relatively enduring., so we can conclude:

Hypothesis 6 (H6): SB has a significant impact on the acceptance of new media entertainment among elderly.

Hypothesis 6a (H6a): SB has more impact on the acceptance of new media entertainment among elderly living without children than those living with children.

Perceived financial cost

PFC in the study is deﬁned as the extent to which an older customer believes that purchasing or using new media entertainment will lead to financial expenses. Luarn and Lin (2005) believed that economic motivations and outcomes were most often the focus of information system acceptance studies, especially the studies on online purchasing and new technology acceptance. PFC was also taken as an antecedent of the behavioral intention to adopt a new technology, such as online shopping (Kallweit, Spreer, & Toporowski, 2014). It was posited that age differences were ascribed to aging identity differences (e.g., communal vs. aging orientations and included personality, attitudinal, financial status, and behavioral differences) (McGaughey, Zeltmann, & McMurtrey, 2013; Tauringana, Good, & Fitch, 2015). Amin, Lada, Hamid, and Tanakinjal (2005) indicated that financial cost and time cost were important factors of purchase decision-making among older adults. Thus, the hypotheses are provided as follows:

Hypothesis 7 (H7): PFC has a negative impact on the acceptance of new media entertainment among elderly.

Hypothesis 7a (H7a): PFC has a more negative impact on the acceptance of new media entertainment among elderly living without children than those living with children.

The framework of our research is shown in Figure 1.

Figure 1.

Research model. TAM = technology acceptance model; eWOM = electronic word-of-mouth.

Methodology and Data Collection

Sample Collection

The model involves nine variables and each variable was measured with multiple items. All items were adapted from established measures of constructs from management information system and behavior literature with adaptations so as to be applicable and content validity for the context of our proposed model. These items were first translated into Chinese by a related researcher. Then, another researcher translated them back into English to ensure the consistency. When the instrument was developed, it was tested among five old male and five old female customers with new media entertainment experiences. Then, according to their comments, some items were modified to improve the clarity and understandability of the questionnaire. The final items and their sources are listed in “Appendix.”

Data were collected through spot survey. First, 10 postgraduates of business were hired and trained as interviewers to understand the purposes of our research. Second, each interviewer was sent to the spot where older adults gathered and completed the interview according to the questionnaire. A total of 500 responses were collected. Among these responses, 32 were excluded because of missing or inappropriate data, and 67 were excluded because they were provided by respondents who had no contact with new media entertainments. Finally, 401 were deemed as qualified responses, which fulﬁlled the requirement of sample size (Hoelter, 1983). More details about the respondents are shown in Table 1.

Table 1.

Demographic Characteristics of Respondents (N = 401).

Demographic	Category	Frequency	Percentage
Gender	Male	187	46.63
Gender	Female	214	53.37
Age (years)	55–59	143	35.66
	60–64	97	24.19
	65–70	104	25.94
	>75	57	14.21
Living arrangement	With children	243	60.60
	With spouse	125	31.17
	With nursing home	5	1.25
	Others (etc. with friends)	28	6.98
Education	Middle school or less	102	25.44
	Senior high school	251	62.59
	college	37	9.23
	Master or doctor	11	2.74
Income ($)/month	500 or less	143	35.66
	500–1,000	203	50.62
	1,000–2,000	49	12.22
	>2,000	6	1.50
New media entertainment awareness	Don’t know	27	6.73
	So-so	76	18.95
	Know a little	145	36.16
	Know quite well	153	38.15
Internet usage frequency	A few times a month or less	11	2.74
	A few times a week	26	6.48
	Everyday	118	29.43
	Many times a day	246	61.35

Measures

The independent variables taken in this research are from the previous reports (Davis, 1989; Henderson & Divett, 2003; Liu, Datta, & Rzadca, 2013; Luarn & Lin, 2005). The independent variables were measured on a 7-point Likert-type Scale ranging from 1 (strongly disagree) to 7 (strongly agree). A total of 21 items were used to measure independent variables. The dependent variable was from the previous report (V. Venkatesh and Davis, 2000). The survey questions are listed in the Appendix.

Results

Two-step approach should be implemented in the analysis of the results. First, validity and reliability were examined. Second, research hypotheses and model fitness were examined (Steenhaut & van Kenhove, 2006).

Assessment of the Measurement Model

The reliability and validity of the study were assessed to evaluate the measurement model in terms of item reliability, convergent validity (individual item reliability), and discriminate validity. Convergent validity indicates whether chosen items can effectively reflect the corresponding factor. Discriminant validity indicates whether each pair of factors are significantly different (Yurdugul & Alsancak Sarikaya, 2013). The reliability test was performed by Cronbach’s α and composite reliability (CR) to examine the internal consistency within a construct.

The standardized item loadings, the average variance extracted (AVE), composite reliability, and Cronbach α values were examined (Table 2). Most item loadings were larger than 0.7 and the least loading was 0.67. t Values indicated that all loadings were significant at 0.001. All AVEs were larger than 0.5 and all CRs were larger than 0.7. Therefore, the scale had a perfect convergent validity (Landeta, 2006). Discriminant validity was examined with the square root of AVE and correlation coefficients. The square root of each AVE was significantly larger than its correlation coefficients with other factors and the discriminant validity was acceptable (Yurdugul & Alsancak Sarikaya, 2013) (Table 3).

Table 2.

Standardized Loading, AVE, CR, and α Values.

Latent variable	Item	Standardized loading	AVE	CR	α Values
PU	PU1	0.732	0.64	0.82	.77
	PU2	0.699
	PU2	0.711
PEOU	PEOU1	0.722	0.69	0.86	.85
	PEOU2	0.745
	PEOU3	0.727
PE	PE1	0.732	0.66	0.87	.84
	PE2	0.724
	PE3	0.748
TR	TR1	0.732	0.71	0.87	.84
	TR2	0.715
	TR3	0.688
eWOM	Ewom1	0.74	0.73	0.76	.78
	eWOM2	0.699
	eWOM3	0.722
SB	SB1	0.801	0.62	0.78	.77
	SB2	0.796
	SB3	0.741
PFC	PFC1	0.689	0.77	0.85	.84
	PFC2	0.712
	PFC2	0.723
IE	IE1	0.735	0.76	0.87	.85
	IE2	0.698
	IE3	0.713

Note. Results of the reliability test. AVE = average variance extracted; CR = composite reliability; PU = perceived usefulness; PEOU = perceived ease of use; PE = perceived enjoyment; TR = trust; eWOM = electronic word-of-mouth; SB = social benefit; PFC = perceived financial cost; IE = intention on new media entertainment.

Table 3.

Discriminant Validity Test.

	PU	PEOU	PE	TR	eWOM	SB	PFC	IE
PU	0.821
PEOU	0.033	0.782
PE	−0.0421	0.241	0.771
TR	−0.146	0.315	0.238	0.752
eWOM	0.332	0.064	−0.245	0.327	0.719
SB	0.154	0.166	0.161	−0.154	0.266	0.783
PFC	0.097	0.284	0.286	0.093	−0.074	−0.144	0.801
IE	0.032	0.339	0.123	0.074	0.202	−0.097	0.101	0.723

Note. AVE = average variance extracted; CR = composite reliability; PU = perceived usefulness; PEOU = perceived ease of use; PE = perceived enjoyment; TR = trust; eWOM = electronic word-of-mouth; SB = social benefit; PFC = perceived financial cost; IE = intention on new media entertainment.

Assessment of the Structural Model

The independent relationships among variables proposed in the study model were assessed by formulating the structural model with software LISREL. Table 4 lists the recommended and actual values of some fit indices (D. Baumgartner, Schulz, & Seidl, 2013; Landeta, 2006).

Table 4.

The Recommended and Actual Values of Fit Indices.

Fit indices	χ²/df	GFI	AGFI	CFI	NFI	SRMR	RMSEA
Recommended Value (H. H. Baumgartner & Homburg, 1996)	<3	>0.90	>0.80	>0.90	>0.90	<0.05	<0.08
CFA	2.12	0.943	0.918	0.94	0.94	0.045	0.052
Structural model	2.09	0.924	0.917	0.94	0.94	0.047	0.054

Note. Values in bold type along the diagonal indicate the square root of the AVE. For discriminant validity, these values should exceed off-diagonal correlations. χ ² /df = ratio between chi-square and degrees of freedom; GFI = goodness of fit index; AGFI = adjusted goodness of fit Index; CFI = comparative fit index; NFI = normed fit index; SRMR = standardized root mean quare residual; RMSEA = root mean square error of approximation.

Table 5 shows path coefficients (PCs) and their significance. The assessment was based on the calculation of the standardized coefficients (β), which indicated the strength of the relationship between two variables (Yurdugul & Alsancak Sarikaya, 2013). The PCs with their significance levels were examined to evaluate the partial least squares of the structural model and hypotheses. Bootstrapping was performed to test the statistical significance of each PC using t tests (Goh & Sun, 2014). The hypotheses, PCs, and t values for both types of living arrangement are listed in Table 5. H1, H3, and H6 were supported, while H2 was not supported, H4, H5, and H7 were partially supported.

Table 5.

Hypotheses, Path Coefficients, and t Value.

	Living without children			Living with children
	Path coefficient	t Statistics	Support or not	Path coefficient	t Statistics	Support or not
PU → IE	0.446**	3.001	H1: Yes	0.421**	3.021	H1: Yes
PEOU → IE	0.419	2.565	H2: No	0.256	1.555	H2: No
PE → IE	0.322**	2.254	H3: Yes	0.317**	2.207	H3: Yes
TR → IE	0.327*	3.122	H4 Yes	0.155	1.446	H4: No
eWOM → IE	0.348*	2.635	H5: Yes	0.185	0.876	H5: No
SB → IE	0.365*	2.224	H3: Yes	0.342*	2.269	H3: Yes
PFC → IE	0.324*	3.148	H4 Yes	0.149	1.441	H4: No

Note. PU = perceived usefulness; PEOU = perceived ease of use; PE = perceived enjoyment; TR = trust; eWOM = electronic word-of-mouth; SB = social benefit; PFC = perceived financial cost; IE = intention on new media entertainment.

p < .05, *P < .01, **P < .001

The variance explained (R2) in the endogenous variables and the regression coefficients’ significance provide an indication of the performance of the study model (Chin, 1998). All the study models have adequate predictive power for most of dependent variables. The intensity of path differences between both groups was tested statistically with t tests and a pooled estimator for the variance. The procedure to develop a multigroup analysis was adopted (Keil et al., 2000). The results are described in Table 6. Four hypotheses out of the five were supported by results.

Table 6.

Comparing Intensity of Path Differences Between Female and Male Customers.

		Standard errors (SE)
		Without children (1)	With children (2)	S_P	PC_1	PC_2	PC_1–PC_2	t Values	Support
PU → IE	1>2	0.125	0.137	0.123	0.446**	0.421**	0.025	7.24	H1a:Yes
PEOU → IE	1>2	0.132	0.122	0.138	0.419	0.256	0.163	12.34	H2a:Yes
PE → IE	1>2	0.121	0.124	0.121	0.322**	0.317**	0.005	8.44	H3a:Yes
TR → IE	1>2	0.165	0.141	0.157	0.327*	0.155	–	9.630	H4a:Yes
WOM → IE	1>2	0.157	0.125	0.132	0.348*	0.185	–	8.528	H5a:Yes
SB → IE	1>2	0.162	0.111	0.138	0.365*	0.342*	0.023	9.37	H6a:Yes
PFC → IE	1>2	0.136	0.137	0.202	0.324*	0.149	–	8.21	H7a:Yes

*p < .01. **p < .001. p < .05.

Both H3 and H3a were supported and all older adults take perceived entertainment as an important factor. Older customers living without children showed more interests in using new media entertainment than others (PC_1–PC_2 is 0.005, p < .001). The similar result had been fully discussed previously (Ryu et al., 2009). H4 was partially supported in our research. It was supported for older adults live without children by 0.327 (p < .01) but failed for those who live with children by 0.155 (p < .05), which lead to the support H4a. Not surprised, the same scenario happens to H5 and H5a, which means older customers live without children has less support to value a new media entertainment from the help of their children but have to depend on it WOM online.

Both H6 and H6a were supported, as expected. Older customers living without children showed more interests in using SNS of online bookstores than males (PC_1–PC_2 is 0.023, p < .001). The similar result had been fully discussed previously (Hustad & Teigland, 2008; Rolland & Parmentier, 2013). The last factor, PFC, played the same role as trust and eWOM in the acceptance of new media entertainment by unsupported H7 and supported H7a. PC of PFC to target was only 0.149 (p < .05) for older adults living with children samples and 0.324 (p < .01) for the other group. H7a was supported which shows older adults live without children have more concern and more sensitive on the cost of new media entertainment than the other group.

Discussion and Conclusion

Theoretical Implications

This study introduced an elderly specific construct to investigate an online elderly user’s intention to accept new media entertainment. Several key findings can be concluded from this study. As hypothesized, according to conventional TAM-related constructs, there was significant support for PU, PEOU, and PE. In addition, our results showed that the extent of a match with their education and income do not directly affect the acceptance of new media entertainment but are rather mediated by types of living arrangements.

First, the results of this study are somewhat consistent with previous studies (H.-S. Kim, Lee, Kim, & Kim, 2014; Renaud & Van Biljon, 2008; Tauringana et al., 2015). The variables in the study model were positively correlated to the acceptance of new media entertainments among older adults aged above 55 years. These results provide support for the existing research that older online customers are likely to adopt new media entertainment if it provides PU and perceived ease to use combined with SB, trust, and less financial cost (McGaughey et al., 2013; Renaud & Van Biljon, 2008).

Second, this study tried to investigate the determinants that affect the acceptance of new media entertainment among older adults. The previous study focused on the acceptance of new technologies with TAM and TAM2 and has less considered the differences in living arrangement among Asian older customers, which is critical to understand their online behaviors. In this regard, this study contributes to existing literature by providing new insights into acceptance behavior among older adults and their effects on the acceptance of new media entertainments.

Third, living arrangement was considered as a novel interesting mediator which affects the acceptance of new media entertainment among the elderly. This study indicated that the effects of PU and PEOU were fully mediated by living arrangement. Living arrangement can be viewed as a specialized example of external variables in TAM, thus these constructs (like TAM’s external variables) would not exhibit any direct relationship with usage acceptances or usage behavior (Chen & Chan, 2014). However, our results implied that living arrangement could be adopted as main constructs in the studies on the acceptance of new media entertainment among older adults. The results also partially supported our assumption that health conditions and other conventional variables might not be influence in the study on the acceptance of the online activities among the elderly. Older adults with different living arrangement behave differently in terms of the acceptance of new media entertainment (Table 6). Older adults living without children behave much more sensitive to SB, trust, and PFC than those living with children.

Practical Implications

From a managerial point of view, this study offers salient insights into the acceptance of new media entertainment among older adults. Based on the analysis of acceptance behaviors of older customers, it was found that living arrangement was a key mediator of the acceptance of new media entertainment. Main external variables such as PU, PEOU, PE, trust, SB, ,eWOM and PFC are effective on older adults. These findings have relatively strong implications for practice and marketing strategies of new entertainment among older adults. It shows that a single strategy to influence the acceptance of new media entertainment on older customers may not achieve expected results. The research provides evidence for practitioners that living arrangement should be taken as an essential market segmentation attribute. From a cultural perspective, the two online purchasing patterns could potentially infer subculture trait differences between older adults as we expected and also supported by Goh and Sun (2014). Our research indicated that strategies that enhance and expand the PU and SB through the endorsement of new media entertainment during promotion and service design. Other approaches that maximize trust issues such as websites safety can be used to build confidence on all older adults, which shows less importance among younger (Benedicktus, Brady, Darke, & Voorhees, 2010). Furthermore, older customers living without children, online activities, including online entertainment can offer personalized features such as IT support services during the purchasing process to minimize the anxiety caused by procedures. Recommended strategies for creating these distinct features include using factual appeals and emotional appeals for older customers in the service design and promotion of new media entertainment products. Our sample shows older customers live with children will be less affected by the perceived ease to use and PFC than others. Therefore, older customers living with children are less motivated by techniques and less sensitive to price. Therefore, practitioners should invest less to enhance perceived ease to use and PFC for them, but strengthen that for others.

Future Research and Limitations

The expenditure of TAM and TAM2 in studying cross senior citizens in using information technologies may receive some criticisms (Goh & Sun, 2014; Ponte et al., 2015; Yang, 2013). Purchase intention or acceptance of new technologies in a speciﬁc background, such as among older customers, change and evolve over time. It also may have influenced by physical conditions, political changes, social factors, and increasingly integrated global economies. This study provides a basis for future comparative studies, including comparing the relative relevance and robustness of TAM and TAM2 with the recently developed approaches, such as SB (Aghekyan-Simonian, Forsythe, Suk Kwon, & Chattaraman, 2012; Babin & Babin, 2001; Bai, Yao, & Dou, 2015) for measuring living arrangement (Lai, 2005; Zimmer, 2005) for Chinese. Moreover, future studies may repeat this study’s research model under different regional, living arrangement settings such as examining Western countries versus Eastern countries, senior citizens versus young people, or under different national economic environments such as developed versus developing countries.

This research has the following limitations. First, we conducted this study in China, where new media entertainment developed rapidly but it was still in its early stage. Therefore, our results need to be generalized to other countries. Second, we mainly conducted a cross-sectional study. However, users, behaviors are dynamic and a longitudinal research may provide more insights on user behavior development. Finally, the explained variance of purchase intention was about 65%. Consequently, besides PU and SB, there exist other factors possibly affecting new media entertainment acceptance behaviors, such as satisfaction and service quality.

Appendix

Measurement Scales and Items

Perceived usefulness (PU), adapted from Henderson and Divett (2003); Pantano and Naccarato (2010)

PU1: New media entertainments are interesting.

PU2: New media entertainments give me entertainment that I cannot get elsewhere.

PU3: New media entertainments are useful for me.

Perceived ease to use (PEOU), adapted from Davis (1989); Henderson and Divett (2003)

PEU1: New media entertainments should be easy to use.

PEU2: New media entertainments can be easily downloaded and operated.

PEU3: Do you need help during new media entertainment operations?

Perceived enjoyment (PE), adapted from Ryu et al. (2009)

PE1: Using new media entertainment is enjoyable.

PE2: Using new media entertainment is pleasant.

E3: Using new media entertainment is fun.

Trust (TR), adapted from Liu et al. (2013)

TR1: New media entertainment is safe for me.

TR2: New media entertainment providers keep their promises.

TR3: My Internet security is guaranteed by entertainment providers and Internet supporters.

Electronic word-of-mouth (eWOM), adapted from Yang (2013)

eWOM1: I will feel good when I can tell others about my great online entertainment experience.

eWOM2: I intend to share my experience with others in the future.

eWOM3: I intend to say good things about the online entertainment with real friends.

Social benefit (SB), developed by authors.

SB1: I like exchanging and looking for help from friends online.

SB2: Sharing entertainment experiences with friends makes me happy.

SB3: I like consulting with friends online when I have problems with online entertainment.

Perceived financial cost (PFC), adapted from Luarn and Lin (2005)

PFC1: It is costly in money and time for entertainment online.

PFC2: It is cost less money to entertain online than offline.

PFC3: There are more financial barriers (e.g., Internet access fees and software costs) in new media entertainments.

Intention to accept new median entertainment (PI), adapted from Chumpitaz Caceres and Paparoidamis (2007)

PI1: I have an intention to entertain online if it is recommended by my friends online.

PI2: I choose to accept my friend’s recommendations or reviews on online entertainment.

PI3: It is likely that I will actually spend money on online entertainments if it is easy to use.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper was financially supported by Humanities and Social Sciences Key Research Program of Department of Education of Zhejiang Province by 2018GH016.

ORCID iD

Dawei Liu

Author Biographies

Dawei Liu is a professor of Hangzhou Dianzi University, China. He was awarded his PhD in Management in Harbin Institute of Technology, China. His research extends into e-commerce, marketing, and enterprises development. He is also a senior researcher of Alibaba Group, China’s largest e-commerce company. He has published more than 20 papers in journals such as Electronic Markets, Information System and E-business Management, Information Development, and so on, and he is a reviewer and associate editor of Electronic Commerce Research and Electronic Commerce Research and Application, etc.

Anqi Liu is a master student of Hangzhou Dianzi University. Her research interest covers e-commerce and information system management.

Wanying Tu is a master student of Hangzhou Dianzi University. Her research interest covers online consumer behavior and information system management.

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