Purpose – Research on technology acceptance involves one's psychological aspect, known as technology readiness. Particularly in the digital acceptance context related to mobile advertising, this psychological condition is referred to digital readiness. Nasution, Rusnandi, Qodariah and Arnita (2018) argue that digital readiness is a prominent factor in the adoption of technology and digital applications. They have proven the importance of this digital readiness in their research on digital mastery level in a telecommunication company in Indonesia. The purpose of this paper is to investigate the effect of digital readiness on the acceptance toward mobile advertising among millennials in Bandung, Indonesia. Relevant theories – Mobile advertising is part of digital advertising, as stated by Nasution & Aghniadi (2016), they define mobile advertising as a form of digital advertising that has attentions on engaging a strong communication to audience. This type of advertising adoption is still continuously growing and becoming preferable form of advertising for the Millenials. Researchers developed a model that links the influence of digital readiness to mobile advertising acceptance. In addition to these relationships, the research model they developed from the Technology Acceptance Model also included the influence of perceived usefulness and perceived risk to mobile ads acceptance. Design/methodology/approach – The research model is developed from Technology Acceptance Model (TAM) with a specific attention to assessing whether digital readiness influences the respondents’ perception of usefulness and risk of mobile advertising. The research performs quantitative approach using survey that is formed based on previous literature and conceptual model. Structural equations modelling (SEM) is also conducted to test the constructed model and the proposed hypotheses. Byme (2010) states the significance of the estimated coefficients for the hypothesis relationships which indicate whether the relationship between constructs held true or not. This research will then compare between three models that are differed by range of respondents’ ages. First model will be combined age that is 17-24 years old, second model is 17-20 years old and third model is 21-24 years old. Findings – The results show that digital readiness has a significant influence towards mobile advertising acceptance among Millennials, in which action readiness is more considered than attitudinal readiness in terms of further assessing mobile advertising. In addition, the study also illustrates Millennials’ perception of usefulness and risk of receiving mobile advertising. Younger group (17 – 20 years old) will not be affected much by usefulness of mobile advertising rather than the older group who will consider much about usefulness on accepting mobile advertising. The group also will take risk along with their ability to accept mobile advertising. It contradicts with the older group (21 – 24 years old) who see risk as hindrance in accepting mobile advertising. Research limitations/implications – The area coverage of respondents only included several cities across Java and does not concern about the place of origin of respondents. Besides, this research also possesses an age limit for its respondents that range from 17 to 24 years old to limit the diversity of attitudes, beliefs and perceptions. Originality/value – This study focuses on the concept of Technology Acceptance Model (TAM) in which the digital readiness is applied in the context that has not been conducted in Indonesia. Researchers conducted an empirical study on the effect of digital readiness on mobile advertising that is part of digital technology. The results of this research provide opportunities for digital readiness applications in research on the adoption of other digital technologies. Paper type - Applied research

This research was conducted in order to examine the effects of user socio-demographics and recently introduced streamlined technology readiness index TRI 2.0 (Parasuraman & Colby, 2015) on mobile device use in B2B digital services. Mobile adoption has been studied from a consumer perspective, but to the best of the authors’ knowledge, very few studies explore mobile use in B2B markets. Mobile marketing is becoming a strategic effort in companies, as digital services not only in B2C but also in B2B sector are getting increasingly mobile (Leeflang, Verhoef, Dahlström & Freundt 2014). This raises an interest to better understand the characteristics of those mobile enthusiasts who primarily use B2B services via a mobile device rather than via a personal computer. The study tests hypotheses with a large data set of 2,306 business customers of which around 10 percent represent these innovative mobile enthusiasts. Technology readiness is an individual’s propensity to embrace and use new technologies for accomplishing goals in home life and at work (Parasuraman & Colby, 2015; Parasuraman, 2000). Parasuraman and Colby (2015) recently introduced an updated version of the original Technology Readiness Index (TRI 1.0) scale called TRI 2.0 to better match with the recent changes in the technology environment. At the same time they streamlined the scale to a compact 16-item version so that it is easier for researchers to adopt it as a part of research questionnaires. Likewise the original scale, TRI 2.0 consists of four dimensions: optimism, innovativeness, discomfort, and insecurity. Optimism and innovativeness are motivators of technology adoption while discomfort and insecurity are inhibitors of technology readiness, and these motivator and inhibitor feelings can exist simultaneously (Parasuraman & Colby, 2015). Optimism is a general positive view of technology containing a belief that technology offers individuals with increased control, flexibility and efficiency in their lives. Innovativeness refers to a tendency to be a pioneer and thought leader in adopting new technologies. Discomfort reflects a perception of being overwhelmed by technology and lacking control over it. Moreover, insecurity reflects distrust and general skepticism towards technology, and includes concerns about the potential harmful consequences of it. As individuals differ in their propensity to adopt new technologies (Rogers, 1995), the authors propose that technology readiness influences mobile device use of B2B customers: H1: Optimism has a positive effect on mobile device use of B2B digital services. H2: Innovativeness has a positive effect on mobile device use of B2B digital services. H3: Insecurity has a negative effect on mobile device use of B2B digital services. H4: Discomfort has a negative effect on mobile device use of B2B digital services. The earlier literature argues that socio-demographic factors such as gender (Venkatesh & Morris, 2000; Chong, Chan & Ooi, 2012), age (Venkatesh, Thong & Xu, 2012; Chong et al., 2012; Kongaut & Bohlin 2016), education (Agarwal & Prasad, 1999; Chong et al., 2012; Puspitasari & Ishii 2016) and occupation (Okazaki, 2006) influence technology adoption behavior in general, and mobile adoption in particular. For example, men are nearly twice as likely as women to adopt mobile banking, and age is a negative determinant (Laukkanen, 2016). Higher educated use mobile devices more for utilitarian purposes, while lower educated use mobile devices more for entertainment (Chong et al., 2012). Moreover, research suggests that occupational factors influence mobile use (Okazaki, 2006). The authors hypothesize: H5: Males are more likely than females to use mobile device for B2B digital services. H6: Age has a negative effect on the use mobile device for B2B digital services. H7: Customers with higher education level have a higher likelihood for using mobile device for B2B digital services than customers with lower education level. H8: Occupation has an effect on the use mobile device for B2B digital services. The study tests hypotheses with a data collected among B2B customers of four large Finnish companies, all representing different industry fields. The large sample (n=2306) consists of procurement decision-makers all experienced with using B2B digital services. The sample shows that over 90 percent of the B2B customers are still using a computer (laptop or desktop computer) as their primary access device for digital services in their work. The sample divides between females and males in proportion to 46 and 54 percent respectively. University degree represents a majority with 42 percent, while only 2,7 percent of the respondents have a comprehensive or elementary school education. Over half of the sample represent top management or middle management with 24,6 and 28,4 percent respectively, while 9 percent are entrepreneurs, 21,2 percent represent experts, and 16,7 percent are officials or employess. Mean age of the respondents is 51,6 years, ranging from 18 to 81 years. The study uses logistic regression analysis with backward stepwise method in which the dependent variable is a dichotomous binary variable indicating the respondent’s primary access device for B2B digital services with 0=computer and 1=mobile device. As for the independent variables, the study measures individual’s technology propensity with recently introduced 16-item TRI 2.0 scale from Parasuraman and Colby (2015) using a five-point Likert scale ranging from Strongly disagree=1 to Strongly agree=5. The authors used confirmatory factor analysis to verify the theory-driven factor structure of the TRI 2.0 scale, i.e. optimism, innovativeness, discomfort, and insecurity. The analysis show that the measurement model for the TRI 2.0 scale provides an adequate fit and standardized regression estimates for all measure items exceed 0.60 (p<0.001) except for one item in discomfort (β=0.516) and one item in insecurity (β=0.480). After removing these two items the model shows an excellent fit with χ2=478.033 (df=71; p<0.001), CFI=0.965, RMSEA=0.050. Moreover, discriminant validity is supported, as the square root of the average variance extracted (AVE) value of each construct is greater than the correlations between the constructs (Fornell & Larcker, 1981). In addition, composite reliability values vary from 0.726 to 0.852 supporting convergent validity of the TRI 2.0 factors (Table 1). Thereafter, the factor scores of the latent factors showing sufficient internal consistency were imputed to create composite measures. These composite measures were used as independent variables in the logistic regression model. With regards to socio-demographic variables, age is measured as a continuous variable, while gender, education, and occupation are categorical independent variables in the model. The results of the logistic regression analysis show that innovativeness, insecurity, age, and occupation are statistically significant predictors of mobile device use in B2B services, supporting hypotheses H2, H3, H6, H8. The stepwise analysis procedure removed optimism (p=0.860), education (p=0.789), gender (p=0.339), and discomfort (p=0.159) from the model as they proved to be non-significant predictors of mobile device use. The results indicate that occupation is the strongest predictor for mobile device use in B2B digital services so that the top management has the greatest likelihood as the odds ratios of middle management, experts, and officials/employees are 0.610, 0.282, and 0.178 respectively. This means that, for example, the odds of the top management using mobile device as their primary channel for B2B digital services are 1.64 (1/0.610) times greater than the odds of the middle management, and 5.62 (1/0.178) times greater than the odds of the officials/employees. Interestingly the β-value for the entrepreneurs is positive indicating that their likelihood for mobile device use is even greater than the likelihood of the top management. However, the p-value (0.913) indicates that the difference is not statistically significant. With regards to age of the B2B customer, the results indicate a negative relationship with mobile device use. The odds ratio [Exp(β)=0.979] claims that the odds of a B2B customer to use mobile device as the primary channel for digital services decrease by 2 percent for each additional year of age. Regarding the TRI 2.0 constructs, the results show that innovativeness is a highly significant positive predictor for mobile device use, while perceived insecurity has a negative effect (Table 2). Literature suggests that B2B customers increasingly use mobile devices but yet little is known about those individuals most enthusiastic in using B2B digital services via a mobile device. Thus, the current study attempts to better understand those mobile enthusiasts who among the first have adopted mobile devices as their primary method to access B2B digital services. The results suggest that occupation is the most significant predictor of mobile use among B2B customers, implying that top managers are among the most likely to adopt and use mobile device for business services. Moreover, younger B2B customers use mobile devices more eagerly as the results suggest the likelihood for mobile device use degreases by 2 percent with every added year of age. The results further imply that out of the four TRI 2.0 dimensions innovativeness and insecurity influence in the mobile device use of B2B customers, innovativeness positively and insecurity negatively as the theory proposes. Innovativeness represents individual’s tendency to be a pioneer and thought leader in terms of technology adoption, while insecurity stems from the general skepticism and distrust of technology. These results imply that B2B customers who mainly access B2B digital services via a mobile device are open minded towards the possibilities new technologies can provide for them. Moreover, it appears that those B2B customers still accessing digital services primarily via a computer are more skeptical than mobile users towards technology in general. Compared to the use of mobile devices for individual purposes, business related use is more functional in nature, and thus, mobile devices and technologies must be convenient to use, offer real benefits for example in forms of mobility and portability, and be reliable in order for B2B customers to use them. Interestingly, our results do not support the effects of generally positive attitudes towards technology reflecting optimism, or discomfort of using technologies to influence mobile use among B2B customers. In addition, there are organizational factors (e.g. voluntariness of use) that the authors omit in the current study. These may limit the findings. Mobility will be a key driver in the ongoing digital revolution of marketing and sales. Understanding online behavior of mobile enthusiasts assists B2B marketing and sales leaders to plan and implement more effective mobile marketing strategies. Rogers (1995) has shown that the majority will follow the early adopters, and the adaptation cycle has even shortened during the last years (Downes & Nunes, 2014). Thus, mobile devices are evidently becoming the primary method in accessing B2B digital services.

The purpose of this study was to examine English lecturers’ readiness toward internet use in English teaching and learning at selected universities in Jambi, Sumatra, Indonesia. A questionnaire was used as a research instrument to sixty-five participants who were recruited through a variety of networking sources, but forty-seven participants completed and returned the questionnaires. Data were analyzed by using descriptive statistics, Pearson Product Moment Correlation, and a t-test. The major findings indicated that the English lecturers’ readiness toward Internet use for teaching and learning was at an average level. This study also found that there was a significant positive correlation between the lecturers’ background of internet use and the level of lecturers’ readiness toward Internet use. There was a positive correlation between lecturers’ knowledge readiness and attitude readiness. However, there were significant differences in the level of readiness between English lecturers at public institutions and private institutions. The findings of this study shed light on policy makers and leaders’ understanding of English lecturers’ readiness toward internet use in English teaching and learning in Indonesian higher education. Policy implications and future research are also discussed.

The purpose of this study is to analyze the level of science and technology development and digital readiness of scientific research institutes based on the author’s assessment methodology (a set of evaluations and multifactorial indicators). Keeping with the previous literature, the research is caused by the novelty of the problem, which suggests dividing the literature review into two main research groups: theoretical background, which concentrates on the digital readiness definition, and the impact of digital readiness on science. Moreover, the scientific significance lies in the fact that proposed ideas in the research, developed theoretical and methodological provisions can significantly enrich theories related to the identification of the digital readiness of science and its consumers. Further, the research is devoted to the development of assessment methods of digital changes and analysis of the level of development of digital readiness of scientific research institutes, which is based on the author’s assessment methodology (a set of evaluations and multifactorial indicators). The methodology provides an opportunity to build ratings of the digital readiness of scientific-research institutes to the formation and development of a digital economy. Obtained results show that the priority task in the current and the future period is to increase the authority and recognition of scientific organizations, the quality of scientific research, and the formation of demand for scientific products.