Predictors for the Adoption of Open Data Technologies: Drawing Upon the Unified Model of Electronic Government Adoption

Open Data initiative is attracting considerable interest globally due to growing phenomena of transparency, accountability, quality of life, and business. Adoption of open data technologies is inevitably an issue to better exploit full potential and benefits of open data available to the public. The main issue in our knowledge of open data technologies is the scarcity of research studies on adoption of open data technologies. Thus, the foremost aim of this study is to predict and explain the predictors that influence adoption of open data technologies. The UMEGA theory was employed as a lens to examine the influencing factors including additional factors i.e. imitating behaviour of others, disregarding own beliefs, and grievance redressal. A survey questionnaire was used to collect the data from citizens and analyzed using PLS-SEM technique. Satisfactory results are obtained proving that facilitating conditions has significant positive influence on effort expectancy, effort expectancy on performance expectancy, performance expectancy on attitude, and attitude on behavioural intention to adopt open data technologies even though the number of participants are very small. Future researchers should find more concrete evidences upon collecting large number of responses.

Introduction
Open data has become a stimulus for public sector organization to publish their factual, objective and nonperson-specific data that is freely accessible to all and usable without any copyright restrictions (Bertot, Gorham, Jaeger, A Sarin, & A Choi, 2014;Hossain, Dwivedi, & Rana, 2015).
Scholars outline that open data refers to an activity undertaken by the organizations or public to visualize, understand, analyze and use the data provided to the public by the public sector agencies (Zuiderwijk, Janssen, & Dwivedi, 2015). The footstep was first taken in the Europe in Public Sector Information directive in 2003 (Attard, Orlandi, Scerri, & Auer, 2015). The open data movement gained significant momentum after the U.S. President Barack Obama's open data initiative in 2009. Later, Australian Open Government Declaration in 2010 (Chatfield & Reddick, 2016), the Open Government Partnership (presently 75 countries are part of it) in 2011, and G8 Open Data Charter in 2013 (Attard et al., 2015) came into existence. However, it was technologically recognized and proposed by Sir Tim Berners-Lee (Hossain et al., 2015). In this connection, Open data portals or technologies are being introduced to provide means for free access to public sector data by all primary and secondary stakeholders.
Currently, governments of developed and developing countries are mandating the public agencies to share data with the public to reuse or distribute without any copyright obligations in order to increase transparency and accountability. Now, Open data has not been exclusive to the open government only, it has also captured the Science, Libraries, Ecosystems, and Economics disciplines as well. Moreover, national, regional, and municipal organizations are also joining the open government data movement. However, opening of some raw data or datasets (integrated set of related data) in machine-readable form on the internet for public use is not sufficient, it is also quite necessary to provide datasets those can be functionally integrated and enhanced to extract values. Open data initiative improves the delivery of public services through effective and efficient data management. This also provides an improved open data infrastructure within government to allow data to be readily accessed, used, re-sued and re-distributed without any restrictions or legal obligations by relevant stakeholders and be integrated with other datasets.
In recent years, data are becoming available by the public entities to the public openly in machinereadable formats to meet the demands for primary and secondary stakeholders whereas these stakeholders can use, reuse and distribute it without any restrictions of copyright or any other legal obligation (Gonzalez-Zapata & Heeks, 2015). However, availability of data by public entities and usage by public is enabled by open data technologies. Therefore, open data technologies enable value creation by both the primary and secondary stakeholders (Gonzalez-Zapata & Heeks, 2015). Value in the context of data enabled by open data technologies for secondary stakeholders such as citizens implies that it enables improving quality of life of the citizens, better understanding of government actions that directly affect the citizens, improved decision-making in their daily-life activities, and innovation and development by analyzing data through its extraction and transformation (Máchová, Hub, & Lnenicka, 2018;Susha, Grönlund, & Janssen, 2015). For instance, citizens may use open data technologies education-related open government data for viewing schools in her surroundings and understanding government policies for developing educational policies country-wide (PDP, 2018), scientists may use open data technologies to obtain rainfall data for the purpose of making modeling, simulations, visualizations and improved methods for accurate predictions of disasters (Younis, Majid, & Ammar, 2017), developers can develop interactive web and desktop applications (Susha et al., 2015), data analysts can make better business decisions by integrating government data with business data, tourists can explore new regions for expedition, students can perform policy research and write academic publications (Zuiderwijk et al., 2015), named a few.
Policymakers want citizens to adopt open data technologies widely, therefore, they need to understand what drives citizens to adopt open data technologies widely (Weerakkody, Irani, Kapoor, Sivarajah, & Dwivedi, 2017). A study on investigating the factors or predictors of open data technologies adoption helps policymakers to exploit value of this innovation. Moreover, understanding the factors influencing adoption of open data technologies will support public entities to make more informed future investment decisions, such as investments in terms of employing specialized staff to open the data, in terms of building infrastructure, hardware and software, time for policy and legislation, reducing the cost of travelling, offering more open data technologies, reducing the development cost of open data technologies (Kassen, 2018;Khurshid, Zakaria, Rashid, Kazmi, & Shafique, 2019;Máchová et al., 2018), and so on. Therefore, this research attempts to fill the gap in knowledge and practice by examining the factors that influence citizens' adoption of open data technologies by employing the UMEGA theory .

Proposed Research Model for Adoption of Open Data Technologies and Hypothesis Development
The open data within public sector is considered under the umbrella, a subset, a sub-domain or an extension of e-government (Attard et al., 2015). Open data technologies are mostly offered by the public sector institutions (Hossain, Dwivedi, & Rana, 2016) to enhance transparency, to release social and commercial value, and to provide opportunities to the public in government's decisionand policy-making (Attard et al., 2015). These technologies are to be used by different stakeholders including citizens. In this context, a Unified Model of Electronic Government Adoption (UMEGA) is employed as a lens to examine the influencing factors of adoption of open data technologies by the citizens. The UMEGA model is a recent model of electronic-government adoption. The distinction between the UMEGA model and other adoption models such as the TAM theory (Davis, 1989), and the UTAUT theory (Venkatesh, Morris, Davis, & Davis, 2003) is that it is designed, developed and validated purely for e-government (Kirat Rai, Ramamritham, & Jana, 2020; Verkijika & De Wet, 2018). The UMEGA model provides a comprehensive evaluation of previously nine well-known theoretical models, combined the constructs together and then rigorously validated it by combining outperformed variables. In presenting the unified model for the e-government adoption, it includes attitude as a protuberant component of e-government adoption (Nguyen, Dang, Van Nguyen, & Nguyen, 2020) as well as facilitating conditions as an antecedent of effort expectancy. Another reason is that the UMEGA model performed better than other technology acceptance and adoption models in the e-government context (Mensah, Zeng, & Luo, 2020).

Figure 1:
The UMEGA Model  Performance Expectancy (PE): A similar construct has been used in developing the UTAUT theory (Venkatesh et al., 2003) as well as its extended version, that is, the UTAUT2 theory (Venkatesh, Thong, & Xu, 2012) to study consumer's adoption of a technology. This construct has appeared as a critical predictor in understanding system's usefulness in earlier studies (Talukder, Shen, Hossain Talukder, & Bao, 2019). Further, a positive and significant influence of performance expectancy on behavioural intention to use digital payments adoption has also been observed in earlier studies (Rahman, Shafique, Khurshid, Asghar, & Ghafoor, 2020). The individuals' attitudes are shaped when they expect open data technologies to be beneficial in achieving more values. In this study, performance expectancy is considered to form the citizens' attitudes. Hence, the formulated hypothesis is outlined as follows: H1: PE has a positive influence on attitude (ATT) toward using open data technologies.

Effort Expectancy (EE):
The users' perceptions about a technology vary and contingent upon its associated simplicity. A large number of users will be using open data technologies more when they evaluate them simple or easy to use in terms of finding, visualizing, accessing, and processing datasets.
The easier the open data technologies are to understand the faster they are accepted and used by the individuals (Weerakkody, Kapoor, Balta, Irani, & Dwivedi, 2017). There are evidences found that the huge efforts restrict the individuals to adopt e-government (Verkijika & De Wet, 2018). However, the associated simplicity with open data technologies is positively linked with their adoption (Saxena & Janssen, 2017). Moreover, effort expectancy is corroborated in earlier studies with respect to e-government adoption Mensah et al., 2020). Following the previous evidences, following hypotheses have been proposed.

Social Influence (SI):
This construct measures the individuals' perceptions about the influence of other individuals on adoption of technologies. It is possible that the social conditions also contribute in shaping individuals' attitudes to adopt a technology besides technical condition such as PE and EE factors. The influence of peers, friends, or family members determine a user's perception to use a technology. The social factor construct the individual behavioural patterns and treated as a strong predictor of public sector big open data . In earlier studies, scholars are largely hypothesizing and corroborating the influence of social conditions on technologies adoption such as e-government adoption (Avazov & Seohyun Lee, 2020;Mensah et al., 2020;Verkijika & De Wet, 2018), adoption of transactional services in egovernment  as well as adoption of open data technologies (Khurshid, Zakaria, Rashid, & Shafique, 2018;Saxena & Janssen, 2017;Zuiderwijk et al., 2015). Based on the facts, the proposed hypothesis is outlined as follows: H4: SI has a positive influence on ATT toward using open data technologies.

Facilitating Conditions (FC):
The available resources such as Internet, computer systems, networks, and public outlets are the prerequisites for using government services. The individual's ability to use the open data technologies is affected by the provision and availability of relevant resources. Further, the knowledge and expertise also induce the individuals to use technologies because these are also the fundamental elements. Thus, skills and expertise, ICT infrastructure, appropriate datasets, and education are vital constituents of open data technologies (Saxena & Janssen, 2017;Zuiderwijk et al., 2015). The absence of such facilities severely affects as well as make the technology adoption tough.

H5: FC has a positive influence on BI toward using open data technologies.
H6: FC has a positive influence on EE toward using open data technologies.

Perceived Risks (PR):
Due to the expected concerns, individuals may not be willing to adopt open data technologies. In the context of e-government, risks are considered as concerns by which an individual may suffer in some sort of losses or uncertain situations while adopting it (Verkijika & De Wet, 2018). For instance, they might expect that there are payment of fee in accessing the datasets and cost might be escalated unexpectedly as more and more datasets are accessed (Janssen, Charalabidis, & Zuiderwijk, 2012). They might have threat of lawsuits in utilizing datasets and participating in open data initiatives. The individuals might also have Internet-related risks like disclosure of their identities on the web and, therefore, limit themselves in using web technologies (Verkijika & De Wet, 2018). The poor information quality may also lead to user's anxiety in taking decision to adopt open data technologies . It can be inferred that the less the risks the more the open data technologies will be adopted. There are evidences found in previous literature where risk perceptions construct is indicated as a significant factor that effect in shaping individuals' attitudes Mensah et al., 2020;Verkijika & De Wet, 2018). Therefore, the following hypothesis has been formulated based on the previous evidences in the literature:

H7: PR has a negative influence on ATT toward using open data technologies.
Governmental Support (GS): Government support is described as "the initiatives of government in creating and enabling environment for the adoption of ICTs in the everyday lives of citizens". Governments is one of the largest stakeholders that can support for using open data technologies. Since governments are providing the public data through web so as to make arrangements and facilities such as ICT hubs, service centers, computer labs, or internet cafes to utilize technologies for accessing the data. Governments also make arrangements to hold workshops to create awareness and train public regarding utilization of open data technologies. In a study conducted by Wang and Lo (2019), scholars found that government support is a crucial predictor of using open government data. However, in this study, government support is considered to have its influence on shaping citizens' attitude and this constructing their intention to use open data technologies. Thus, the following hypothesis is formulated: H8: GS has a positive influence on BI toward using open data technologies.

Imitating Others (IMO):
A technology will be adopted by a unit of adoption when it is evident that someone is using it observing the benefits (Vinnik, 2017). Similarly, the benefits are also become evident to others while it has been using for some time. Skeptical users learn from other's observations and deduce results regarding adoption value (Sun, 2013). It is also possible that users are not able to collect information properly about technology benefits and imitate other's behaviour in adopting it (Vinnik, 2017). There are found evidences in earlier studies in which similar factor has been found to have a positive influence on user's behavioural intention (Sun, 2013;Vinnik, 2017) as well as users' attitudes . Based on the facts, in this study, the below hypothesis is formulated:

H9: IMO has a positive influence on BI toward using open data technologies.
Disregarding Own Beliefs (DOB): Discounting or disregarding self-opinion about technology adoption is based on one's self-observation of other's behaviour on technology adoption. Disregarding own belief is not a self-instructed belief of users rather it is based on self-observation (Vinnik, 2017). People follow others and discount their own beliefs when a technology is adopted by a large number of users. They prefer others and choose the technology to utilize it. Thus, they discount their own preferences in adopting a technology. In earlier literature, scholars have observed its moderating effect on changing users' beliefs (Sun, 2013). A similar construct, that is herd behaviour, has been found significant to have its influence on forming user's attitudes in intention to use transactional services . Another study has also framed this factor in measuring citizen's adoption of OIoTD (Open Internet of Things Data) platform offered by the public sector . Therefore, accordingly, the following hypothesis has been formulated: H10: DOB has a positive influence on BI toward using open data technologies.

Attitude (ATT):
The level of positive appraisals or evaluations affect the individuals' intention to use services and technologies. There are plenty of evidences in earlier literature which validate the association between attitude and intention in the e-government context Kirat Rai et al., 2020;Verkijika & De Wet, 2018). Accordingly, it is hypothesized that the high intention to use open data technologies is influenced by the individual's positive evaluations or appraisals.

Figure 2: The Research Model -Extended UMEGA
The influence of performance expectancy, effort expectancy, social influence, and perceived risks are examined on citizens' attitude whereas facilitating conditions is hypothesized as the predictor of both effort expectancy and behavioural intention. Effort expectancy is also hypothesized to have an influence on performance expectancy. Further, government support, imitating others, disregarding own beliefs, and attitude are hypothesized to have their influence on behavioural intention. A complete model is depicted in Figure 2.

Methods
Pakistan is a developing country which is at initial stage with respect to open data technologies. Pakistan was selected in order to test and validate the UMEGA theory and its extended version. This administrative region was selected because researchers found it easy to collect suitable data. Another reason is its participation in Open Government Partnership initiative. However, the uptake of open data technologies is very slow in Pakistan.
A quantitative study was adopted to test the UMEGA theory and to empirically investigate the formulated hypotheses. A survey questionnaire approach was used to collect the data. All the constructs and their items in the questionnaire were adopted from previous studies and provided in Appendix A. These studies included Kumar, Adlakaha, & Mukherjee, 2018;Saxena & Janssen, 2017;Sun, 2013;Talukder et al., 2019;Vinnik, 2017;Zuiderwijk et al., 2015). Data was collected from the citizens in Pakistan through the online developed survey. The data collection process was not outsourced rather the data were collected through self-administered online questionnaire only. A total of 54 citizens provided their feedback using online survey and the data collection task was carried out during the month of September 2019. Respondents were asked to mark their level of agreement against each question based on seven-point scales ranged from "Extremely Disagree" to "Extremely Agree". A small introduction about open government data was presented before the respondents to give them an understanding about open data technologies. The UMEGA theory was extended to determine the factors which influence the adoption of open data technologies .
The sampling technique that was adopted in this research was random. Since, 100% of respondents of this survey were well-educated, this suggested a high degree of competency to answer the questionnaire. Therefore, this provided us confidence in evaluating the suitability of respondents for this study.

Demographics
Of the study population, 77.8% are males and 22.2% are females. A major chunk of respondents (55.5%) are 30-39 years old, followed by 22 Cronbach's Alpha, composite reliability, content validity, and convergent validity must be greater than 0.6 whereas AVE must be greater than 0.5 for evaluation of structural model (Hair, Hult, Ringle, & Sarstedt, 2017;Nunnally & Bernstein, 1994). Since all the values are greater than the minimum acceptable value, structural model has been evaluated to test the hypotheses (see Table  1 and Table 2).

Structural Model
In structural model, explanatory factors (R 2 ) and significant values (p values) are the most crucial values to assess the structural model. the values of R 2 0.02, 0.13 and 0.26 are categorized as low, medium and high explanatory variance (Hair et al., 2017). The R 2 value of effort expectancy is 0.321, performance expectancy is 0.393, attitude is 0.553, and R 2 value of behavioural intention is 0.638 (see Figure 3). It is found that there is high explanatory variance, since the R 2 value of all these constructs are greater than 0.26. The p values less than 0.05 show the significant values and acceptance of hypothesis.

Discussion
This study has given an account of predicting citizens' adoption of open data technologies. The results have strengthened our confidence in hypothesis that PE is the most significant predictor of ATT followed by FC on EE and followed by ATT of the behavioural intention to use open data technologies (Saxena & Janssen, 2017;Talukder et al., 2019;Zuiderwijk & Cligge, 2016;Zuiderwijk et al., 2015). Being H1 to be positive and significant indicates that citizens would be forming positive ideas about open data technologies if these technologies enable them to complete their tasks quickly and find useful in their daily lives which, in turn, have an influence in building citizens' attitudes. Being H3 to be positive and significant reveals that the more the citizens feel simple and easy to adopt open data technologies, the more useful they find these technologies in their daily lives. Being H6 to be positive and significant shows that it would be simple for citizens to adopt open data technologies if they find necessary resources, knowledge, and infrastructure. Moreover, being H11 to be positive and significant means that citizens' positive and negative evaluations about open data technologies would form their willingness, plan, and likelihood of using them (Saxena & Janssen, 2017;Talukder et al., 2019;Zuiderwijk & Cligge, 2016;Zuiderwijk et al., 2015).
The results are distinguishable from UMEGA  where PE, EE, SI, and PR are found to be the significant predictors of ATT whereas FC is the significant predictor of both EE and behavioural intention. Therefore, the results are not in good agreement with UMEGA  at this stage where there are low number of participants. Apart from these disagreements, the results are expected to confirmation of UMEGA Model on large number of respondents. These results, thus, need to be interpreted with caution of limited number of participants.
Our study has been unsuccessful in proving that DOB, FC, GS, IMO, and PR have significant impacts on behavioural intention to use open data technologies. It is also unsuccessful in proving that EE and SI have significant impacts on ATT. In this context, there is, certainly, a large room for improvements in studying the influencing factors on adoption of open data technologies by increasing the number of responses. Therefore, discrepancies in results may be negligible since the sample size is very small in measuring the determinants of adoption of open data technologies.

Conclusion
To


Towards the Development of 3D Engine Assembly Simulation Learning Module for Senior High School

The focus of the study is to develop a 3D engine assembly simulation learning module to address the lack of equipment in one senior high school in the Philippines. The study used mixed-method to determine the considerations needed in developing an application for educational use particularly among laboratory/practical subjects like engine assembly. The study used ISO 25010 quality standards in evaluating the application(n=153 students and 3 ICT experts).Results showed that the application is moderately acceptable(overall mean = 3.52) under ISO 25010 quality standards. The study created an engine simulation learning assembly in which teachers can use to augment their lesson. The study also highlights the applicability of using 3D-related technologies for practical and laboratory subjects particularly highly technical-related subjects. Future studies may develop a similar application in the same context using mobile and other emerging technology(i.e., Virtual Reality, Augmented Reality) as well as making the content more customizable. Effectivity of the system in an actual setting is also worth pursuing. The study highlighted the potential use of 3D technology in a classroom setting.

INTRODUCTION
The simulation program is used to facilitate students in their learning by mimicking the actual scenario or situation. This allows the student to work, learn, experience even without the supervision of their professor or teacher (So, Chen, & Wan, 2019;Lateef, 2010). In the recent event, where the world is experiencing a health crisis due to the COVID-19 pandemic, the education sector is looking for different ways to promote education in distance learning. Furthermore, teachers, instructors, and professors are engaging in ICT integration in teaching the students to protect them from COVID-19.
According to Barbosa (2015), there is an increasing trend in the use of 3D and simulation technologies due to the declining cost of computers as well as the constant increase in processing power. Barbosa (2015) also mentioned that this kind of technology with the right interface and environment can be an asset for the teaching and learning process. Furthermore, simplicity, ease of navigation, and the human-computer interaction aspect of 3D simulation tools allow a student to engage, increase enthusiasm, and better focus towards the learning objective of an activity. This was supported by Azer and Azer (2016) whereas they mentioned in their study that there is an increasing interest in the use of 3D models as evidenced by a large number of publications related to 3D technology. Azer and Azer (2016) further elaborated that 3D models in both traditional and digital formats are favored by most students from healthcare institutions as it can improve their spatial visualization skills.
In the Philippines, TVL (Technical-Vocational-Livelihood) automotive track is one of the tracks in K to 12 that is continuously growing in the student population. Its goal is to prepare their graduates with enough skills to help them generate their income without the choice of pursuing college. TVL Automotive is implemented in the school year 2016-2017. It has 6 strands namely: agricultural fishery, home economics, information and communications technology, industrial arts, applied and specialized subjects in arts and design, and applied and specialized subjects in sports. Although TVL automotive is selected by many students, TVL automotive still has many problems that the schools and the track itself encountered on its implementation, a large portion of this problem is directly attributed to lack of resources.
The uniqueness of the study among the studies previously conducted is that the study intended to develop an application in consideration with the learner's context particularly their needs concerning the Philippine K to 12 educational settings. Thus, the main objective of the study is to develop a learning module using 3D technology to enhance the capability of the school and the teachers in teaching the core competency needed to acquire the TESDA NC II in Service Engine Mechanical System of TVL Automotive students. Specifically, it aims to (1) provide students a platform to simulate and teach mechanical engine assembly, (2), determine the contents of the system, and (3) evaluate the said system using ISO 25010 quality standards.

LITERATURE REVIEW
Technology brought many advantages especially when the world is facing a global health crisis such as COVID-19. It provides different systems and applications that may help the different sectors of society, businesses, government, and educational institutions. One of the emerging technologies is the use of 3D related applications where Bhavani, Sheshadri, and Unnikrishnan (2010) mentioned the vast potential of such technologies varying from 3D web-based interactive learning application for medical practitioners (Violante & Vezzetti, 2017), 3D Simulation modeling technology for architectural design (Omar, El Messeidy, & Youssef, 2016), and 3D simulation system (Salman & Hussein, 2016). This kind of technology allows the enhancement in the ability of vocational education as well as making it accessible to economically challenge communities and helping the learners in grasping complex skills and concepts. Huang and Gui (2015) described that developing an application using a 3D environment has a huge economic benefit. It enabled students to put their knowledge into practice in an integrated way such as acquiring transferable competencies, providing new and engaging learning opportunities, and a way to practice safe real-life professional situations (Cela-Ranilla et al., 2014). Such applications also allow asynchronous learning of laboratory experiments at their own home (Salman & Hussein, 2016) and at the same time apply existing theories, explore, and make their own decisions (Koivisto et al., 2017) without being exposed to hazards (Zhu, 2017). Furthermore, Bai et al. (2012) said that using 3D simulation for instruction helps visual learners promote self-paced learning that provides realistic situations. It is also one of the affordable options to engage students in problem-solving and critical reasoning (Bai et al., 2012) which helps them refresh their knowledge in various situations (Violante & Vezzetti, 2017).
While the impact of 3D applications on learning proven to be relatively positive. In one study, Korakakis et al. (2012) confirmed the contribution of the 3D learning environment in the learning process. Another study showed that using 3D applications in educational settings reduces the learning curve among students (Hu et al., 2018). Additionally, the increase in usage of such applications is directly correlated to the academic performance of the students (Hanna, Richards, & Jacobson, 2014). The engagement and interest among students tend to be higher when the 3D learning environment is used (Can & Simsek, 2015;Paton & Houghton, 2012) as most students have a presumption that using 3D learning applications the experiences gap from virtual and reality can be significantly reduced (Can et al, 2015).

METHODOLOGY
Mixed-method was used in the study, a series of interviews and observations were conducted to understand more of the problems encountered by both teachers and students in one senior high school in Pampanga, Philippines. This public school was selected due to the number of students enrolled in the TVL Automotive track. Interviews with the teachers and assessors are carried out to determine which part of the NC II core competency test of the service engine mechanical system should be added to the application. The response of the teachers and assessors were transcribed and group together to form the main concepts which determine the most needed features of the 493 application, how the application should look like, its core contents, and which specific mechanical parts are to be added in the application.
Towards this goal, the application was developed using C# programming as its base language as well as Unity 3D and SketchUp for the overall interface and 3D models (i.e., machine equipment, engines, and mechanical tools). For the technical specification, the following were used: 64bit Windows 10 operating system, 2.4GHz Intel i7 5 th Gen. processor, 8 Gb of RAM, and 2 Gb dedicated Graphics card. While Agile model (i.e., Requirement Analysis, Design, Development, Quality Assurance, and Deployment) was adopted for the development process to ensure the suitability of the learning module. The requirement analysis, design, development, and quality assurance phases were conducted simultaneously before the deployment of the learning module for the alpha testing. The alpha test was administered to determine if the application follows the standard set by the ISO 25010. Three ICT experts and a total of 153 students enrolled in the TVL automotive track evaluated the application using a 5-point Likert scale.

RESULTS AND DISCUSSION
Based on the interviews, the application should be divided into four (4) main categories namely: Module, Tools, Assemble, and Test category as seen in Figure 1. These sections were highly sought by the informants.  Figure 2 shows the category is further subdivided into seven (7) sections, the first part contains all the video demonstrations (i.e., conditions before assembly, disassembly and reassembly procedures, inspections and requirements, and functional check and adjustments) 494 required by the informants to be added in the module. This section is added to ensure that different mediums for learning are present inside the application.

Figure 2. Module Section
The general module section contains the brief description and specific specification of varying parts used for engine servicing (e.g., valve spring. Piston, and connecting rod). While the engine block (Figure 3), the main moving system (Figure 4). The intake and exhaust system (Figure 5), cylinder head assembly (Figure 6), and timing system ( Figure  7) sections cover the learning materials used by the informants which are approved by the technical education authority.   The Tools category contains the specific tools used in engine servicing, it is subdivided into six (6) sections: personal protective equipment (PPE), lubrication equipment, engine assembly, and disassembly tools, engine repair and repower tools, and the engine reassembly and reinstallation. Each section contains 3D animated models and a brief description as seen in Figure 8; these contents are validated by experts in the field.  Figure 9 shows the interface where learners and teachers can simulate engine assembly. The window has three (3) main parts. From the left side shows the different tools that can be used to assemble the engine, the bottom part shows the steps needed to assemble the engine, and lastly the middle part where the user learners can simulate the engine assembly. Basic mouse and keyboard controls (i.e., Q -rotate to the X-axis, W -rotate to the Y-axis, E -rotate to the Z-axis) could be used to rotate the 3D objects. The learners may also opt to use the numeric keypad to change their perspective (i.e., Front, Back, Top, Bottom, Left, Right).  Table 1 illustrates that the application is rated as moderately acceptable by its intended users (i.e., students) and by the ICT experts in the alpha testing. It also means that the application follows the ISO 25010 quality standards.

CONCLUSIONS AND RECOMMENDATIONS
Since most of the public schools contain one or more computer laboratories before the implementation of K to 12 curricula, the study underscores the need and applicability 499 of using digital learning modules to enhance the capability of teachers in teaching technical skills and solves the deficiency of equipment and tools which can be used by students. It is also worth mentioning that the students and teachers both agreed that the application is suitable and helpful together with the other learning materials towards learning and understanding their lessons.
For future studies, it is recommended that additional car types and parts could be added to the application given the growing variety of automobiles in the industry as well as making it customizable and a mobile version of the application is also worth pursuing. Also, future researchers can study the effectiveness and impact of the developed application by subjecting it to Usability Acceptance Testing (UAT) in the actual setting. Additionally, they should also look at the potential use of augmented, virtual, and/or mixed reality to the field, particularly in the test/assessment section.

IMPLICATIONS
The study showed that public education institutions could harness the potential of emerging technologies like 3D as a way to teach students in learning laboratory and practical subjects.


extfieldsofstudy:
Psychology
Computer Science

text:
Can Narrative Advertisement and eWOM Influence Generation Z Purchase Intentions?

Digital platforms have made Generation Z able to listen to the story from both sides, i.e., the brand’s promotional messages, and the consumers’ experiences. To capture an audience’s attention on endless entertainment and informational platforms, narrative advertisement is deployed to trigger emotions and feelings. As digital communities continue to grow, both brands and consumers are using narrative advertising to share their opinions. Hence, the purpose of this research was to investigate how the upcoming generation of consumers will be affected by the two information sources, i.e., the brands via narrative advertising, and peer consumers through electronic word-of-mouth (eWOM). Furthermore, the mediated role of persuasion knowledge was examined among the constructs. Theoretical foundations were empirically tested using quantitative research analysis. The data was collected from a sample of 304 Generation Z respondents from Pakistan. Structural equation modeling (SEM) using AMOS 22.0 was executed to determine cause and effect relationships. This study offers new evidence regarding the effective mobilization of eWOM and narrative advertising in the context of Generation Z. The results indicate narrative advertising to have strong effects on Generation Z purchase intentions as compared to eWOM.

Introduction
The recent innovative developments have mobilized the marketing efforts of global firms to spark the excitement of digital natives (i.e., Generation Z) with real-time and narrative content [1,2]. The possession of distinct beliefs, preferences, and values is engraved in almost every generational cohort which eventually forms different behaviors. These evolving dynamics have led marketers on diverse marketing strategies, as a generic marketing mantra would not be impactful for all generations [3]. Each generation witnesses its unique technological evolution, one generational cohort could prefer the experience of brick-and-mortar where they can touch and feel the products, while another might prefer the convenience of an online retailing store [4,5]. Bashir et al., [4] suggested that each generation has its own way of shopping, time allocation for it, and selection of brick-and-mortar or online store and that brands must adapt to it accordingly. Chu and Kim [6] explained that while recognizing the generational differences, brands may change their hedonic/utilitarian use of digital platforms and tools, the data sources they employ, the influence of these platforms on a purchase, and different payment options. The upcoming generation of consumers that marketers need to focus on is Generation Z. Generation Z or "Digital X" is the generational cohort of consumers born between 1995 and the late 2000s [7]. Generation Z is the first generation to grow up with technology from their early childhood years, there they are more sophisticated in terms of connectivity and information as compared to any prior generation ever [8]. They are relatively intense users of digital platforms and tools, concerning the number of hours spent and the number of platforms they use as well [9].
Narrative or storytelling ads are one example of such ads that tend to generate stronger emotional responses and consumer engagement [10]. Recent studies on narrative advertising found that story ads are more probable to trigger emotions in viewers, also facial coding has shown that there are more chances that viewers will give expressions while watching a narrative format ad [11,12]. Generation Z use more digital channels as compared to their predecessors and their attention is divided between various devices and they prefer quick solutions as they have unlimited information access [13]. Thus, advertising to Generation Z is different from other generations. They like the brands and products in which they can see their reflection, and prefer realistic and relatable content. Narrative advertising can make consumers imagine themselves experiencing the given product or service [14]. Henceforth, narrative ads must be investigated in the case of Generation Z as they are an upcoming larger market segment. More and more brands are using narrative advertising to trigger emotional responses in their audience and given the Gen Z online traits and habits, the narrative advertisement could be useful to capture their attention [15][16][17]. The effect of both eWOM and narrative advertisements simultaneously on purchase intentions needs to be measured as both constructs have different conceptions and themes. Through the mediation effects of persuasion knowledge, it would be interesting to determine whether eWOM or narrative advertisement has a more powerful effect on purchase intentions.
Contextually, Pakistan is one of Asia's rapidly growing e-commerce markets [18]. As of July 2020, the Pakistan telecommunication authority (PTA) states that there are 81 million 3G and 4G users in Pakistan [19]. Moreover, there are 83 million broadband users in the country. Meanwhile, the overall cellular subscribers in Pakistan are 167 million. According to Hootsuite [20], there are 76.38 million internet users in Pakistan. As of January 2020, internet penetration in Pakistan has exceeded 35%. Furthermore, there are almost 37 million social media users in Pakistan. In the case of social media users, Pakistan has a 35% yearly growth as compared to a 21% global average. Moreover, as the numbers of consumers using digital media continue to increase, brands have also followed the trend and almost every major brand in Pakistan has a social media presence. Henceforth, due to the aforementioned factors, the investigation of such a study in the Pakistani context is pertinent.
Nguyen, [21] employed only Facebook respondents to investigate the electronic word of mouth (eWOM) effect on electronic product consumers, as the scope of advertising has been widened by social media platforms, more and more marketers are using digital advertising thus the effectiveness of eWOM must also be investigated on other platforms. Khwaja and Zaman [22] also proposed further investigation should be done using different online users falling into diverse age categories and taking other categories of products other than the smartphone to be examined for future research. This could be extended to examine the eWOM impact on generation Z on multiple digital platforms. Menon et al. [23] limited their research to Twitter posts on airline companies and suggested extending the work to other industries and social media platforms. Considering the aforementioned factors, it is theorized that since Generation Z grew up with unique values, beliefs, socio-cultural, technological, and economic environment; thus, their buying behaviors are divergent and need to be investigated. The purpose of this paper is to examine how narrative advertisement and electronic word of mouth (eWOM) are affecting the purchase intentions of Generation Z. Furthermore, also to explore how persuasion knowledge mediates between narrative advertisement and purchase intentions.
The study is oriented to determine how persuasion knowledge would be mediating between narrative advertising, electronic word-of-mouth and the purchase intentions of Generation Z. Positivist research philosophy has been deployed using a deductive approach for the determination of causality. Quantitative research using survey techniques were executed for the collection of data. Statistical analysis using structural equation modeling (SEM) techniques was deployed to precisely measure theoretical association among the constructs.

Narrative Advertising
Narrative advertising is defined as conveying core messages to the customers by telling a story. Globally, narrative advertising and storytelling techniques are deployed in marketing campaigns [24,25]. An advertisement that presents information regarding a product or service in a narrative form is more likely to get a positive evaluation by the audience as compared to the advertisement presenting information more straightforwardly. The principle of storytelling lies in the fact that the human mind stores information in a story format. Liang and Tukachinsky [25] differentiated narrative or non-narrative as non-narrative lack such a narratively arched sequence of linked events and character development; instead, they may present information in a more list-like format or using generalized arguments. Narrative advertising generally depicts the product or service use and its aftercoming or constitutes plots showing the individual's wishes, for instance, accomplishment or adventure [26].
Stories are the essence of human life, Bashir et al. [26] explained that throughout our lives we learn and build ourselves through stories. In an advertising context, many researchers have established the fact that narrative format ads perform better than non-narrative or argumentative ads [27][28][29].
Researchers have also established that narratives are more persuasive and influence the attitude of the audience [30,31] and behavioral intentions [32][33][34] and generate more positive feelings, less negative cognitive responses, and favorable advertisement and brand attitude [35].
Contemporary research studies indicate that digital platforms and tools have ended the era of interruptive advertising and have let consumers take control of when and where to let advertisers interrupt their entertainment [36]. As a result, advertisers are looking for distinct ways to advertise and entertain customers [37,38]. Furthermore, due to changing consumer shopping patterns, consumers can be landed onto the online shop with a more detailed product description and purchase options, just with a single click on the digital ad. Nevertheless, the actual challenge for the advertisers remains that the digital ad should resonate with consumers and thereby capture their attention [39][40][41].
Kim, Maslowska, and Malthouse, [42] stated that watching a narrative makes customers imagine themselves experiencing the given product or service, thus make them absorbed into the story, which could further influence customers' brand attitudes and behavioral intentions. Liang and Tukachinsky [25] researched to examine the effect of narrative reviews on digital platforms when written by an expert and found that the narrative format influences the attitude of the reader more as compared to a non-narrative review. Von Helversen, et al. [43] found that narrative ads generate greater advertising message involvement when compared with a factual ad format. Narrative advertising triggers more emotions in the audience thus can be used by advertisers to create positive emotional affiliations with brands [44].
Lal and Rathore, [45] explored the utility of television commercials in their research to find the influence of narrative ads. As digitization has revolutionized the marketing arena, it would be insightful to investigate the effectiveness of narrative advertising using digital platforms. Mahmood et al. [7] suggested investigating how peers influence teenagers' attitudes towards online narrative advertising, and ultimately their brand attitude thus allowing investigating eWOM influence on the purchases of Generation Z. Bashir et al. [3] emphasized that marketers must investigate to gain a better perspective of consumption patterns of different generations, and how various marketing strategies impact each generation's purchasing behavior.

Electronic Word-of-Mouth (eWOM)
Electronic word-of-mouth is defined as the consumers' behavior of exchanging the product or service-related information with other consumers in various digital communities [46]. The term eWOM is also explained as an opinion or experience whether favorable or not when shared by a current, former, or potential customer about a product or brand using any digital platform that has a wider reach to other consumers [47]. Khwaja et al. [48] explained eWOM as information sharing between two or more than two consumers regarding a product.
Prior research studies have shown that various forms of eWOM positively influence trust among the audience [49], affect consumer attitudes [50], trigger behavioral intentions [51][52][53], and readiness to pay [54]. Ansary and Hashim [55] emphasized that many important consumer decisions are influenced by eWOM, such as the decisions involved in which product or service to purchase. Digital consumers are more aware and have access to a bigger pool of information that helps eWOM become an effective and powerful advertising tool, hence many researchers have investigated its impact on consumer decision making [56,57].
Hussain et al. [58] found that consumers frequently consult online reviews and recommendations while considering a purchase option. The reviews and recommendations of other users that project their attitude towards the product reduce ambiguity in the potential consumer about product quality and attributes and hence reduce risk to facilitate purchasing product decisions [59]. The experience and trustworthiness reflected in an online review influence the significance of eWOM that affects attitude and induce purchase intention [60]. Kudeshia and Kuma [61] found that when favorable reviews and recommendations were given by other users on social media it positively influenced the brand attitude and purchase intention of the reader. Khwaja et al. [62] found a positive impact on a company's stock returns by favorable eWOM in social communities.

Activation and Use of Persuasion Knowledge
Persuasion knowledge represents a consumer's beliefs about the motives underlying the marketing message, the message creator's ability to gain benefit from the message, the appropriateness of specific persuasion tactics, and how to evaluate the value attached to the marketing message [63]. The persuasion knowledge of the consumer is defined as the supposition of the consumer about persuasion, which includes their beliefs regarding the power and appropriateness of the marketing message intention and tactics, along with the ways to resist these persuasion attempts [62]. Further studies describe a consumer with high persuasion knowledge as the individual who can figure out easily when a marketing message is pushing their decision and can identify different marketing tactics used by advertisers.
In this information era, consumers can acquire more prenegotiation information than ever before and this expertise makes them more prone to marketing messages [62]. Among the main elements that contribute to such expertise in the consumer is persuasion knowledge [64]. Zheng and Watts, [65] suggested that by activation and usage of persuasion knowledge a consumer can identify that a marketer is trying to push or manipulate the consumer's decision in their favor. Moreover, consumers learn persuasion knowledge over time, and it can be enhanced. Studies elaborated that improving persuasion knowledge helps in making better decisions and in financial understanding. In the case of digital communications, persuasion knowledge helps in evaluating the reviews and recommendations as to whether they are unbiased or not and helps in reducing vulnerability to such persuasive motives [66].
Friestad and Wright, [66] presented their persuasion knowledge model in the early 1990s, in which they described persuasion knowledge as a person's ability to identify advertising material and to recognize its persuasive motives. A consumer's persuasion knowledge develops over time and it can be activated whenever a consumer is exposed to a marketing message [67]. The use and activation of persuasion knowledge enable the viewer to critically evaluate the content of a marketing message and to identify the persuasive tactics and strategies used by marketers in the advertising material [68].
In the online advertising context, researchers suggested that when consumers were being informed that they were viewing sponsored material or advertisements, it did not make them more critical. The study of Reijmersdal, Lammers, Rozendaal, and Buizen [69] found that through the intervening mechanism of persuasion knowledge, such disclosures led to higher brand recall. There is some evidence that long disclosures can foster persuasion knowledge, which leads to less favorable brand attitudes [70]. When marketers give disclosure of marketing material the activation of persuasion knowledge occurs and that facilitates better attention to the brand placement and increases memory of the brand [71,72], while some researchers found that persuasive attempts of marketing messages to consumers with better persuasion knowledge could trigger an unfavorable attitude regarding the advertised product [73]. Researchers like Hamby and Brinberg, [74] also suggested that when persuasion knowledge was activated among consumers it could lead to counterarguing, and afterward to an unfavorable brand attitude.

Purchase Intention of Generation Z
Every generational cohort goes through unique circumstances and events which constitute unique values, beliefs, and preferences that define their behavior and attitudes. Generation Z is the generational cohort of consumers born between 1995 and the late 2000s [75]. Their predecessor, the Millennials (Generation Y), was technology-driven but Generation Z is the first generation to grow up with technology from childhood. Generation Z is the most connected via the internet, an educated and sophisticated generation ever. The members of Generation Z spend significant periods using digital communities and tools as well as using multiple social communities [76].
Many researchers elucidate Generation Z in terms of their social media habits such as Generation Z that is born between 1995 and 2009, has a significant role in technology in their daily lives, and spends large amounts of time using social media channels on their smart gadgets [77]. Khwaja [78] found that marketers can better engage Generation Z on social media and can influence their buying decisions better through digital platforms. Generation Z follows celebrities on digital media and buys products that are endorsed by them on social media. Similarly, it was found that celebrity endorsement on digital platforms results in more consumer attention than any other digital medium marketing. The study examines purchase intentions as an attitudinal construct. The behavioral aspect of purchase behavior can be further explored; however, the current study restricts it to the purchase intentions context [79]. The orientation thus remains to understand how narrative advertising and eWOM can change Generation Z's buying intentions.

Theoretical Associations among Constructs
Narrative advertisement triggers self-referencing and identification with the principle characters [79]. In recent times, the attention spans of consumers are deteriorating at a rapid pace [80]; also, Generation Z not only has a shorter attention span; they also juggle more screens. Thus, the effectiveness of narrative [80,81] could be investigated in Gen. Z. Hypothesis 1 (H1). Narrative advertisement will positively influence the purchase intentions of Gen. Z.
Generation Z is a hyperconnected generation [81]. The latest Nielsen reports show two-thirds of consumers trust other consumer's reviews and recommendations posted on several digital platforms and is the third most trusted medium. Hussain et al. [58] emphasized that many important consumer decisions are influenced by eWOM, such as the decisions involved in which product or service to purchase. Hence the following hypothesis is proposed: Hypothesis 2 (H2). Electronic word-of-mouth will positively impact the purchase intentions of Gen Z.
It has been established by several researchers that information presented in narrative format has more influence on the audience's attitudes and beliefs [25,30], behavioral intentions [22,26], fosters positive emotions [34] and causes fewer counterarguments [7], while activation of persuasion knowledge plays a strong role in driving the consumer's decision-making process [82]. Kim et al. [42] in their research suggested that the process mechanism of narrative ads can be studied with the mediation role of activation of persuasion knowledge. The following theoretical connections are hypothesized:

Hypothesis 3 (H3).
There will an influence of narrative advertisement on the activation of persuasion knowledge. [82] concluded that positive eWOM messages lead to consumer buying behaviors, while negative eWOM messages reduce the consumer's intention to buy products and services. Besides, Khwaja et al. [13], elucidated that the information adoption patterns of individuals tend to alter if the strength of eWOM is weak. Farzin and Fattahi, [53] suggested that in the case of digital platforms where consumers (who are strangers to one another), communicate; the element of uncertainty is naturally inherent in these settings as if the author of the review is an unbiased consumer or a biased marketing source. By considering the aforementioned theoretical associations, the following hypothesis is proposed: Hypothesis 4 (H4). There will be an influence of eWOM on the activation of persuasion knowledge.

Heejae and Dahana
This recent digital landscape has made consumers able to acquire better prenegotiation information than ever before [78]. Consumers have an unlimited stream of information and brand alternatives. This information era allows consumers access to whether a marketing message is too good to be believed. Thus, in Generation Z how activation of persuasion knowledge affects their purchase intention needs to be explored. The following hypothesis is proposed:

Hypothesis 5 (H5). Activation of persuasion knowledge will influence the purchase intention of Gen Z.
Furthermore, the following two hypotheses are proposed to examine the mediating role of the activation of persuasion knowledge on the relationship of narrative advertising and eWOM with the purchase intention of Gen Z. Hypothesis 6 (H6). The activation of persuasion knowledge will mediate the relationship of narrative advertising and the purchase intention of Gen Z.

Hypothesis 7 (H7).
The activation of persuasion knowledge will mediate the relationship of eWOM and the purchase intention of Gen Z.

Materials and Methods
This research follows a descriptive research design that relates to the projection of participants' experiences. Descriptive research designs are often used by marketers to investigate the traits and habits of consumers. The research followed a positivist philosophy and deployed a deductive approach. Quantitative research was conducted using a structured questionnaire. The research instrument was designed by ensuring that the validity and reliability of all the adapted scales were in the acceptable range. The research instrument comprised twenty-five items and they were evaluated on a 5-point Likert scale. Five items of narrative advertising were adapted from the study of Burnkant and Unnava [83], three items of activation of persuasion knowledge were adapted from the study of Rozendaal, et al. [84], ten items of eWOM were adapted from the study of Zhang and Watts [65], and five items of purchase intentions were adapted from the study of Duffett [1]. The unit of analysis for this study were individuals who belong to Generation Z, involved in online buying in Pakistan. A nonprobability convenience sampling technique was used for the collection of data. The respondents of the study were students studying in different universities in Pakistan. The data was collected offline, by visiting the campuses. For the affirmation of established theoretical association, the SEM technique was deployed. Covariance-based SEM (CB-SEM) using AMOS 22.0 was used as it ensured advanced statistical data modeling in the determination of causal relationships among constructs. Examining data normality, exploratory factor analysis, confirmatory factor analysis, constructs reliability, discriminant, and convergent validity [85,86], and multicollinearity determination were mandatory before testing hypotheses in CB-SEM [87][88][89][90][91]. According to Zaman et al. [92], a sample size of more than 200 respondents is ample for SEM. Meanwhile, a few researchers have suggested a sample size of more than 250 is sufficient for configuring statistical path modeling [88]. Hence, the data was collected from 304 respondents.
Most of the respondents were female, 173, with a valid percentage of 56.9%, whereas male respondents were 131, with a valid percentage of 43.1%. As the data was collected from Generation Z respondents, therefore, 90% of the respondents were within the age limits of 18-23 years (90%), while 9.9% of the respondents were under 18 years old. The internet usage frequency of this generation was found to be extensively high. Some 83.6% of respondents used the internet at least once a day, while almost 11% of respondents used the internet at least once in seven days. The internet shopping frequency revealed that around 35% of respondents purchased products once in 30 days, while 26.3% bought products online once in 90 days. Table 1 provides a complete reflection of the respondents' demographic outcomes.

Results
Multivariate normality was determined, and it revealed that there were no normality concerns in the data. The standard deviations of narrative advertisement (NA), purchase intention of Generation Z (PIGZ), persuasion knowledge (PK), and electronic word-of-mouth (eWOM) were 0.92374, 0.84417, 1.03189, and 0.85724 respectively, which is in the acceptance range of +2 and −2. The skewness values of the constructs NA, PIGZ, PK, and eWOM were found to be 0.166, −0.605, −0.081, and −0.447, respectively, which are also in the acceptable range of +1 and −1. Lastly, kurtosis results of all the constructs were between the acceptable range of +3 and −3. These results notify that there were no normality concerns in the data (see Table 2). Model estimation was done using SEM on AMOS 22.0. Initially, data normality, construct validity, reliability and exploratory factor analysis (EFA) was conducted on SPSS 24.0. After the attainment of positive outcomes of the respective tests, confirmatory factor analysis (CFA) was executed on AMOS. One of the key roles of CFA is a reaffirmation of reliability and validity through composite reliability (CR) and average variance extracted (AVE). CFA outcomes provided in Table 3 manifestly indicate that all the results were in the acceptable range. ρ denotes EFA results, λ presents standardized factor loadings, α illustrates Cronbach's alpha values, CR presents composite reliability values and AVE presents average variance extracted values. All the EFA (P) values were above 0.4, standardized factor loadings (λ) were above 0.3, α and CR values were above 0.7, and AVE values were also above 0.4 (see Table 3 (see also Appendix A), Figure 1). Table 3. CFA/ EFA factor loadings, reliability, and validity of measurement model (n = 304). The configuration of model fitness is another critical aspect of the measurement model. Incremental fit indices and absolute fit indices are the two further divisions of CFA. Standardized root mean squared residual (SRMR), adjusted goodness of fit index (AGFI), root mean square of approximation (RMSEA), chi-square/degree of freedom (χ2/df), and goodness of fit index (GFI) is determined in an absolute fit index. The values of SRMR and RMSEA were 0.055 and 0.059, respectively, which are in the acceptable range (less than 0.08). The χ2/df result was 2.053, which is also in the acceptable range (1)(2)(3). Furthermore, GFI and AGFI values were 0.886 and 0.857, which are also adequate (<0.95). The outcomes of incremental fit indices revealed the TLI, NFI, CFI values to be 0.911, 0.861, and 0.923, which are in the permissible range of less than 0.95 (see Figure 2).  The discriminant and multicollinearity validity was estimated in which variance inflation factor (VIF) results were in the acceptable range of 1-5, and maximum shared variance (MSV) outcomes were also in the permissible range of less than 1. Moreover, square correlations in diagonals revealed that there were no validity concerns in the respective model (see Table 4). The established hypotheses were consequently examined in the structural model where direct and indirect paths were examined. The direct paths of the first five hypotheses were quite significant as H1, H2, H3, H4, and H5 had path coefficients (beta values) of 0.329, 0.318, 0.386, 0.255, and 0.248, respectively, with t-values greater than 1.96, indicating acceptance of these hypotheses. The indirect effects of H6 and H7 provided path coefficient results of 0.227 and 0.256, respectively. All the hypotheses had p-values of 0.00 which indicated the phenomenal significance of the hypothesized paths. Moreover, the regression result (R2) value of purchase intention of Generation Z (PIGZ) was 0.39, and of narrative advertising (NA) was 0.35 (see Table 5). Lastly, the structural equation model fit measures are illustrated in Table 6, indicating χ2/DF, GFI, IFI, CFI, NFI, TLI, AGFI, RMSEA, and SRMR values to be in the acceptable range.  Purchase intention of Gen. Z, R 2 = 0.39, narrative advertising, R 2 = 0.35

Discussion and Conclusions
The results suggest that when the consumer is presented with a narrative advertisement, then it activates the use of persuasion knowledge, and it positively influences the purchase intention of Generation Z. Prior studies have reflected diverse results about the effect of persuasion knowledge, some researchers found its influence negative, while others showed that activation of persuasion knowledge helped increase brand memory and loyalty. Due to the deterioration in the attention spans of consumers, it remains pertinent to investigate whether narrative advertising has a future or not. As of 2020, the attention span of individuals has been reduced to 0.825 seconds. There is an element of storytelling in narrative advertising, which consequently requires longer advertisements [93,94]. Thus, the study oriented to precisely measure whether the size of narrative advertising affects Generation Z as per the theoretical directions provided in the prior studies. The results reveal that Generation Z's purchase intention is influenced when they are presented with narrative advertising. eWOM also plays a significant role when Gen Z make their purchasing decisions. Similarly, eWOM being an interesting evolving phenomenon remains a critical factor to determine along with narrative advertising. Interestingly, the direct effects of eWOM on purchase intentions were slightly less as compared to the impact of narrative advertising. However, the mediation of persuasion knowledge between eWOM and purchase intentions was found to be strong. The Generation Z cohort is quickly taking over the marketplace, and their particular online habits and traits need different marketing strategies to capture their attention. In this context, the research gives insight into how to create an online marketing strategy that resonates with Generation Z. As digital platforms have empowered the consumers in a way that they can control when and where they can avoid the marketing material to interrupt their entertainment, marketers needs to make advertising entertaining in itself. By employing storylines in advertising, marketers can better target this generation, as narrative advertisements can trigger emotions, eventually leading to capturing their attention. Meanwhile, by using the interactive nature of digital platforms, marketers must participate in a two-way dialogue with their audience. As eWOM plays an important role in the decision making of the consumer, digital presence, and positive image are important to capture the attention of Generation Z consumers. The study is beneficial for the practitioners as it would be providing insights to the marketers that how narrative advertising can boost purchase intentions. Generation Z is reckoned to be a difficult generation to target by the marketers; henceforth, the paper provides directions of how Generation Z can be influenced through narrative advertising and eWOM with the mediation of persuasion knowledge. The study would be beneficial for upcoming entrepreneurs too. While designing their marketing strategies, they can make effective use of the cyclical dynamics of open innovation, i.e., deploying eWOM and narrative advertising simultaneously.

Future Research Directions
The research is insightful in understanding narrative advertising and electronic word-of-mouth, and how particularly Generation Z is influenced by these. Yet, certain limitations need to be recognized and thus are presented in this section. Firstly, the research is cross-sectional and it would be more significant to study the same variables in longitudinal research as a better understanding can be achieved through longitudinal research to study the behaviors of individuals. This research also took a generic approach considering the online ads that are being displayed on various digital platforms, the same research could be done under controlled settings. For future studies, the narrative advertising effect could be further examined by using different thought protocols; for instance, how prior brand attitude will affect the narrative advertisement process. Furthermore, antecedents of narrative advertising should be unfolded, and dimensional effect should be determined. The impact of narrative and non-narrative advertisements on the purchase intentions of Generation Z must be explored in future studies. Simultaneously determining the effects of narrative and non-narrative advertisements would provide a concrete holistic picture of the subject matter. In terms of digital interaction, eWOM in generic milieu was considered, while future studies could be conducted in a specific product category, or low and high involvement products and how narrative advertising and eWOM can influence in these settings. Lastly, both the effects of narrative advertisement and eWOM could be studied during an actual marketing campaign. Funding: This research received no external funding.

Conflicts of Interest:
The authors declare no conflict of interest.


s2fieldsofstudy: Business Economics

text:
Discounted Cash Flow Methods in Lease versus Purchase Analysis—A Technical Note

The earlier framework uses the before-tax cost of debt as the discount rate in valuation of lease contracts for the reason that such framework explicitly includes the interest tax credits as a component of each period’s cash flows. The short-cut or modern standard textbook approaches use after-tax cost of debt as the discount rate for the reason that it ignores the interest tax credits. Some existing literatures state the two approaches are equivalent without exploring the reasons analytically. This note provides a mathematical demonstration showing, the two approaches are not equivalent accompanied by numerical examples.

Introduction
When a firm wishes to obtain the use of an equipment, it can either purchase it or lease it. An equipment lease is an agreement in which one party (the "lessor") gives the other party (the "lessee") the right to use the lessor's equipment for a certain period of time. In exchange, the lessee will make payments to the lessor during the lease period. At the end of the period, depending on the lease agreement, the lessee can walk away, or may be given the option to buy the equipment that it has been leasing, or may be given the option to renew the lease for another term.
Purchasing an expensive equipment can use up available funds and may saddle a firm with an outdated asset and leasing can be an excellent way to update the business without significant upfront costs. The other advantages to lessees mentioned in mainstream managerial finance textbooks (e.g., Ross, Westerfield, & Jordan, 2019;Brealey, Myers, & Allen, 2020) include high operating flexibility, risk reduction of technological obsolete and uncertain demand, and most importantly, the tax advantage: A firm that leases equipment or real estate, for example, will be able to deduct its lease payments from its taxable income immediately rather than deducting the cost of purchasing equipment as depreciation over time. Lessors also benefit from leasing in a number of ways. Compared to lessees, they are often able to acquire equipment at a low cost, and obtain acceptable financing terms. These advantages result from various economies of scale, such as increased buying power with sellers of equipment. Lessors are also better positioned to take advantage of certain tax laws, such as depreciation allowances and investment tax credit. The tax advantages to both lessee and lessor largely depend on the tax bracket each is in: the higher marginal tax rate, the larger the tax benefit, other things being equal. The conventional wisdom is that the firm needing to use the equipment lets a firm with a higher effective tax rate purchase it and take the depreciation tax benefit. The asset's ultimate user then leases it from the purchaser under a contract which allows the tax benefit to be shared between two firms.

Review of Lease versus Purchase Valuation
The modern agreed-upon conceptual framework of lease vs. purchase analysis is formulated on the notion that a lease contract is equivalent to a loan. Because the asset user's operating cash flows are not affected by lease or purchase decision therefore may be ignored in a lease vs. purchase analysis. The Net Advantage to Lease (NAL) is the difference between the saved initial investment cash outlay to pay the asset and the present value of the Lease's Net Cash Outflows (LNCOs), where each period's LNCO is the sum of the following three components: (1) the after-tax lease payment; (2) the foregone depreciation tax credit; and (3) the foregone interest tax credit on the debt that the lease is presumed to displace. If NAL>0, the asset user would prefer leasing to borrowing-to-buy.
A lessor's analysis of a proposed lease is a capital budgeting valuation problem. The Net Present Value (NPV) of the project is the difference between the present value of the lessor's net cash inflows and initial investment outlay. The lessor's net cash inflows in each period is the following three components: (1) the after-tax lease revenue; (2) the depreciation tax credit; (3) the interest tax credit on the debt. If NPV>0, lessor benefits from the leasing.
To evaluate lessee's NAL and lessor's NPV, the discounted cash flow (DCF) methods are used. DCF is commonly used to estimate the value of an investment, here the leasing contract, based on its future cash flows. DCF analysis attempts to figure out the value of an investment today, based on projections of how much money it will generate in the future.
A considerable amount of work has been done on the analysis of leasing as a financial decision of the firm. A comprehensive paper by Myers, Dill, and Bautista (1976) discusses the fundamental issues in leasing and presents a thorough analysis of the valuation of leasing contracts based upon fundamental financial principles. Their framework, along with Lewellen et al. (1976), use the before-tax cost of debt as the discount rate because they explicitly include the interest tax credits as a component of each period's cash flows for lessee's NAL calculation. A simpler, "short-cut" approach ignores the interest tax credits and use after-tax cost of debt as the discount rate for lessee's NAL and lessor's NPV calculations, and this short-cut approach has later also become the modern standard textbook lease/purchase framework. A natural question arises: are these two approaches equivalent? Early literatures, such as, Long (1980) and Ezzell and Miles (1983), state that under appropriate assumptions on debt displaced, the two approaches are equivalent. And, recently, Musumeci and O'Brien (2019), also state the two approaches yield the same lessee's NAL, thus are equivalent without exploring the reasons analytically. In next section, we will present a mathematical demonstration showing that the two approaches are not equivalent and material errors can arise accompanying with numerical examples.

A Mathematical Presentation
Let P denote the equipment's purchasing price, depreciation in year k, lease payment in year k, lessor (buyer)'s before-tax cost of debt, lessee's before-tax cost of debt, lessor's tax rate and lessee's. As mentioned earlier, the modern agreed-upon conceptual textbook framework of lease vs.
purchase analysis is formulated on the notion that a lease contract is equivalent to a loan agreement.
Thus, "lease versus borrow-and-buy" is a more accurate description of the modern textbook framework than the simple term of "lease versus purchase" which implies that the lessor uses 100% debt to finance its purchase. The standard textbook framework further assumes the equipment has no salvage value at the end of the lease term. Under these assumptions, using the original framework (MBD/LLM), the lessee's NAL can be expressed as (1) While when the short-cut or standard text book framework is used, the lessee's NAL becomes (1 − ) is lessee's after-tax lease payment, depreciation tax credit and interest tax credit.
For lessor's NPV, theearly framework (MBD/LLM) gives While the short-cut or standard text book framework gives Where (1 − ) is lessor's after-tax lease payment, depreciation tax credit and interest tax credit.
Mathematically speaking, unless the tax rates are zero, equations (1) and (1') are not equivalent, neither are equations (2) and (2'). To illustrate our point, we first use the same example seen in Musumeci and O'Brien (2019) (also in Ross, Westerfield, Jaffe, & Jordan, 2019). In that scenario, the user wants an equipment that costs $10,000 and that may be depreciated straight-line for 5 years ( n D =$2,000) with salvage value of zero.
A leasing company offers to lease the equipment to the user for an annual lease payment of $2393 per year. Lessor and lessee are assumed to have the same interest rates of 6.329% which implies annual interest payment pn I =$632.9, but their tax rates vary: lessee l T =0% and lessor =21%. Since the lessee's tax rate is assumed to be zero, equations (1) and (1') merge, and it is easy to calculate lessee's NAL=$9.40. However, the two approaches yield different NAL if the lessee's tax rate is not zero, for example, 5%, we can calculate the lessee's after-tax cash out flow including interest tax credit in each year: And the present value of this five-year annuity discounted at 6.329% is 10040.70, thus NAL=-40.70.
When using the short-cut or standard textbook framework, the lessee's after-tax cash out flow without including interest tax credit in each year: (1 − ) + =2393(1-5%)+2000x5%=2373.75. And the present value of this five-year annuity of 2373.75 discounted at after-tax rate of 6.01% (6.329%(1-5%)) is 9.996.38, and NAL=3.62. And the two approaches yield different lessee's NAL.
For lessor's NPV, using the original MDB/LLM framework, lessor's after-tax cash inflow in each period is the sum of after-tax lease payment, depreciation tax credit and interest tax credit which is And the present value of five-year annuity of 2943.37 discounted at lessor's before tax debt rate of 6.329% is 12288.36, thus NPV=2288.36. However if we use the standard textbook framework in which the interest tax credit of 632.9 is not included in lessor's cash inflow in each period and the after-tax discount rate is 6.329%(1-21%)=5%. The present value of the five-year annuity of 2310.47 (=2943.37-632.9) discounted at 5% is 10003.13, thus the lesseor's NPV=3.13 which is the same as shown in Ross, Westerfield, Jaffe and Jordan (2019).

Conclusion
This note examines the two widely used frameworks in the valuation of financial lease contracts: the early framework which includes the tax credit in lessee and lessor's annual cash flows and uses the pre-tax discount rate; the short-cut or standard textbook framework ignores the tax credit in lessee and lessor's annual cash flows and uses after-tax discount rate. This note challenges existing literatures' claim that the two approaches are equivalent and demonstrates that there are fundamental difference between the two framework analytically accompanied by numerical examples.