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The substantial expansion of technology and the efficacy of digital platforms in reaching young audiences have led to enhanced targeting and customization of promotional communications. Notwithstanding the expansion and efficacy of contemporary advertising platforms, scholarly attention has not kept pace with this domain of inquiry. This study aims to assess the antecedents of Google Shopping Ads (GSA) on intention to purchase behavior among the Generation Y and Z cohorts.

The current study used a quantitative approach and snowball sampling technique to gather primary data via a questionnaire and Google Forms, which resulted in the collection of 5,808 questionnaires among the cohort members. A principal component analysis and multigroup confirmatory multigroup structural equation modeling (between Generation Y and Z) were used to assess the research data and model.

The results show positive trust and perceived value associations with intention to purchase, particularly among Generation Y and Z consumers. The findings also show negative irritation, product risk and time risk associations with intention to purchase, especially among the Generation Y cohort, which indicates that young consumers generally do not observe perceived risk due to the usage of GSA.

GSA will continue to grow and become an increasingly important integrated marketing communications tool as the digital landscape develops. It can be concluded that young consumers show a high degree of perceived value and low levels of perceived risk due to the use of GSA. This study, therefore, promotes improved understanding among academics, marketers and businesses of search engine advertising among young cohorts of consumers (Generation Y and Z) in a developing country context.

The purpose of this study is to review and synthesize the literature on in-app advertising, identify gaps and propose future research directions.

The authors use a systematic literature review (SLR) approach, following the PRISMA guidelines, to investigate the current state of research in in-app advertising. The study uses 44 shortlisted articles from the Scopus and Web of Science databases. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the authors analyze the gaps in theory, context, characteristics and methods.

Using thematic analysis, the authors identify five main themes in the in-app advertising literature, namely, ad platform optimization; mobile app user psychology and behavior; ad effectiveness; ad fraud; and security, privacy and other user concerns. The findings show the need for empirical research, with a strong theoretical foundation in emerging ad formats of in-app advertising, user behavior and buy-side of in-app advertising.

This is a maiden study to conduct a domain-based SLR in the emerging field of in-app advertising using the TCCM framework. The authors highlight the key differences between in-app advertising and mobile web advertising. The authors propose theories in the advertising field that could be used in future empirical studies of in-app advertising.

El propósito de esta investigación es revisar y sintetizar la literatura sobre la publicidad en Apps, identificar lagunas y proponer futuras direcciones de investigación.

Utilizamos un enfoque de revisión sistemática de la literatura, siguiendo las directrices PRISMA, para investigar el estado actual de la investigación en publicidad en aplicaciones. El estudio utiliza 44 artículos preseleccionados de las bases de datos Scopus y Web of Science (WoS). Utilizando el marco Teoría-Contexto-Características-Metodología (TCCM), analizamos las lagunas en teoría, contexto, características y métodos.

Mediante un análisis temático, identificamos cinco temas principales en la literatura sobre publicidad en aplicaciones, a saber: optimización de plataformas publicitarias; psicología y comportamiento de los usuarios de aplicaciones móviles; eficacia publicitaria; fraude publicitario; seguridad, privacidad y otras preocupaciones de los usuarios. Nuestros hallazgos muestran la necesidad de investigación empírica, con una sólida base teórica en los formatos publicitarios emergentes de la publicidad en Apps, el comportamiento del usuario y el buy-side de la publicidad en Apps.

Se trata de un estudio pionero para realizar una revisión sistemática de la literatura basada en el dominio en el campo emergente de la publicidad en Apps utilizando el marco TCCM. Destacamos las principales diferencias entre la publicidad en aplicaciones y la publicidad en la web para móviles. Proponemos teorías en el campo de la publicidad que podrían utilizarse en futuros estudios empíricos sobre la publicidad en Apps.

本研究旨在回顾和总结有关应用内广告的文献, 找出差距并提出未来的研究方向。

我们采用系统性文献综述方法, 遵循 PRISMA 指南, 调查应用内广告的研究现状。研究使用了 Scopus 和 Web of Science (WoS) 数据库中的 44 篇入围文章。利用理论-背景-特征-方法（TCCM）框架, 我们分析了理论、背景、特征和方法方面的差距。

通过主题分析, 我们确定了应用内广告文献的五大主题, 即广告平台优化; 移动应用用户心理和行为; 广告效果; 广告欺诈; 安全、隐私和其他用户关注点。我们的研究结果表明, 有必要在应用内广告的新兴广告形式、用户行为和应用内广告买方等方面开展实证研究, 并奠定坚实的理论基础。

这是一项首次使用 TCCM 框架对新兴的应用内广告领域进行基于领域的系统性文献综述的研究。我们强调了应用内广告与移动网络广告的主要区别。我们提出了广告领域的理论, 可用于未来的应用内广告实证研究。

This study proposes a hierarchic segmentation that develops a tree-based classification model and classifies the cases into groups. This allows for the definition of e-commerce user profiles for each of the groups. Additionally, it facilitates the development of actions to improve the adoption of the online channel that is in such high demand in the current pandemic COVID-19 context.

Regarding the created segments, two extreme segments stand out due to their marked differences and high volume. Segment 3 with 23% of the sample is the group with the most predisposition to use the online channel and is characterised by a high level of trust, more habitual use in comparison with other groups and the belief that its use implies high performance, which indicates they believe it to be useful, quick and helpful for more an effective shopping experience. The other extreme is found in segment 7. This group makes up 17.7% of the total and is the most reluctant to use the online channel. These users are characterised by the complete opposite: they have a low level of trust in this channel. However, the effort expectancy is low, i.e. they consider that the adoption of the online channel does not involve many difficulties in its learning and use. Nevertheless, they use it less regularly than the others.

Based on the conclusions reached in this study, in the current pandemic context in which consumer demand for online shopping channels for all types of products is on the rise, it is recommended that companies focus on the following aspects. It is essential to build trust with the user and show them the real benefits of e-commerce, how it would improve their life and why they should use it. Additionally, it is vital that the user perceives it as an easy procedure that does not require a significant learning curve. Other fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process, to make it as easy as possible, and to design a simple, intuitive and user-friendly interface. It is also recommendable to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy and assuring them that their information will not be misused nor shared with third parties. All of this provides a series of facilities to modify the online shopping habits of users.

As in most of the research, this study presents a series of limitations that should be debated and that could open future lines of investigation. Firstly, regarding the sample used that was limited to two neighbouring countries with similar profiles a priori; it would be necessary to compare their possible cultural differences according to Hofstede's dimensions as well as increase the number of European countries being analysed to reach a more generalised conclusions. Secondly, the variables used are a combination of those derived from the UTAUT2 model and others suggested in the literature as decisive in technology adoption by users, in this sense other theories and variables could be incorporated to complete a more holistic model.

This work contributes in a general way to (1) analysing the intention to use e-commerce platforms from a set of antecedents previously defined by their importance, after a period of economic and social restrictions derived from the pandemic; (2) determination of customer segments from the classification made by the CHAID analysis; (3) characterisation of the previously defined segments through the successive divisions that were proposed in the analysis carried out.

Other fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process to make it as easy as possible, and to design a simple, intuitive, and user-friendly interface. It is also recommended to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy, and assuring them that their information will not be misused or shared with third parties.

The results obtained have allowed us to establish predictive and explanatory models of the behaviour of the segments and profiles created, which will help companies to improve their relationships with online customers in the coming years.

本研究擬提出一個會發展基於樹的分類模型、以及會把案例歸入不同的類別的層次細分。這讓我們能為每個類別考慮到電子商務用戶輪廓的定義和解釋；這亦促進我們優化採用在線渠道的發展工作,而在線渠道於現時2019冠狀病毒病肆虐的情況下,實在供不應求。

就創設的細分而言,兩個極端的細分因其明顯的差別和大批量而顯得突出。佔樣本百分之二十三的細分3是擁有最大使用在線渠道傾向的細分,而細分3的特徵包括他們對在線渠道呈高信任度,比其他類別更習慣地使用,以及其相信使用在線渠道會帶來更高的績效,這表示他們相信使用在線渠道是有效的,是快捷的,是可幫助帶來成功的購物體驗的。另外的極端在細分7內發現。這類別佔整體的百分之十七點七,而他們是最不願意使用在線渠道的類別。這類別的特徵和前述的剛剛相反：他們對在線渠道的信任程度是低的,唯其努力期望是低的,也就是說,他們認為使用在線渠道是不會涉及很多在學習上或在實際應用上的困難。即使是這樣,他們較其他人卻較少使用在線渠道。

基於研究的結論,我們的建議是：於目前大流行肆虐期間,消費者對於以在線渠道網購各類商品的需求不斷增加,企業應聚焦以下的範疇：企業必須建立消費者對電子商務的信心,並為他們展示電子商務的真正好處；企業也必須使消費者明瞭電子商務如何能改善其生活,以及他們為何要使用電子商務。更重要的是使消費者覺得使用電子商務是輕而易舉的,又不涉及陡峭的學習曲線。凡此種種,就成為消費者改變其網上購物習慣的動力和誘因。至於其他基本的考慮,包括減輕消費者對使用電子商務的不確定情緒,使電子商務易於使用,以及設計一個簡易的、憑直覺能知曉的、方便使用的介面。另外,值得推薦的是、數據使用情況須有效地管理。為此,我們建議應儘量向使用者索取最低限度的資料,為他們提供資料保護政策,保證他們的資料不會被濫用或與第三者分享。

與其他大多數的研究一樣,本研究展現了一系列值得辯論的局限,而這些局限或許會開展未來研究的領域。首先,考慮到使用了一個局限於兩個以因及果演繹而成的、概況相似的相鄰國家為樣本,我們或許需要根據霍夫斯泰德文化維度理論對這兩個國家進行比較,以瞭解它們的文化差異；另外,為求能達致可普遍適用的結論,我們也需把被分析的歐洲國家的數目增加。其次,被使用的變數是兩組變數的組合,他們是從UTAUT2模型中取得的變數,以及在有關的文獻裡,就技術採用而言、使用者認為是重要的變數。就此而言,若其他的理論和變數能被包含其中,則達致的模型將會是一個更為整體的模型。

本研究就一般而言有以下的貢獻：(一) 、 在因大流行病而引起的經濟和社會限制實施時期後,研究人員分析人們如何從一套過去被認定是電子商務平台的重要前身而選擇使用電子商務平台,本研究對這方面的分析作出了貢獻；(二) 、本研究幫助確定從透過CHAID分析而來的分類中得到的顧客細分；(三) 、本研究透過進行連續分解、幫助歸納過去被認定的細分的特徵。

企業必須建立消費者對電子商務的信心,並為他們展示電子商務的真正好處；企業也必須使消費者明瞭電子商務如何能改善其生活,以及他們為何要使用電子商務。更重要的是使消費者覺得使用電子商務是輕而易舉的,又不涉及陡峭的學習曲線。凡此種種,就成為消費者改變其網上購物習慣的動力和誘因。至於其他基本的考慮,包括減輕消費者對使用電子商務的不確定情緒,使電子商務易於使用,以及設計一個簡易的、憑直覺能知曉的、方便使用的介面。另外,值得推薦的是、數據使用情況須有效地管理。為此,我們建議應儘量向使用者索取最低限度的資料,為他們提供資料保護政策,保證他們的資料不會被濫用或與第三者分享。

本研究所得的結果,讓我們可以建立多個模型、以預測並解說有關的市場部分的行為和被創建的消費者簡介,這會幫助企業改善它們今後與網上顧客的關係。

Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.

This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.

The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.

The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.

The purpose of this study is to systematically review available state-of-the-art literature on comparative studies on Quick Commerce (Q-commerce) and E-commerce and their greenhouse gas (GHG) emissions.

The literature survey methodology is based on the funneling approach of Kitchenham (2004), where results are obtained according to inclusion and exclusion criteria. The literature review methodology used for this study covers the period from 2016 to 2022. The areas considered for the survey are operations, logistics and supply chain network design for the distribution of goods in e-business. After deciding on the criteria, a total of 140 articles were extracted from 9 journal articles that study e-commerce and environmental emissions.

The result of this study reveals that GHG emissions from both modes of shopping depend on various parameters such as speed of delivery, last-mile depot locations, logistics and vehicle efficiency, customers’ order patterns and average basket size. Furthermore, the findings also highlight the difference between Q-commerce and E-commerce supply chain networks.

This study only accounts for GHG emissions from logistics activities, but there are other sources of GHG emissions in the overall supply chain that are not taken into consideration. Supply chain/business analysts in Q-commerce companies might refer the findings from this study to measure GHG emissions from their operations.

This is the first study in the Q-commerce field that uses a structured approach to find relevant literature from the years 2016 to 2022 and focuses on GHG emission measurement.

This study analyzes how tech firms can implement the modern wave of subscription-based business model (SBBM), including value proposition, value creation, value capture and performance. In fact, these elements push tech firms to move from traditional to SBBMs.

To achieve the objectives of this study, we initially construct a theoretical framework for applying SBBM. Subsequently, we employ qualitative research to examine the current implementation of the subscription-based economy within tech firms.

A successful SBBM necessitates capturing value through sustainable revenue transactions and revising aspects of the value proposition, creation and capture. Continuous improvement through business value analysis is imperative. Additionally, an agile operations system is vital to address revenue complexities, enable data collection and enhance value proposition, service innovation, churn rate and customer retention, which are essential for SBBM maintenance.

This study delves into how the subscription-based economy is reshaping the business models of tech firms. Beyond exploring the theoretical foundation of this transformative path, this study offers actionable insights on enhancing the value proposition, creation, capture and business value within subscription-based economy frameworks.

In this study, the authors seek to understand factors that naturally influence users to adopt two-factor authentication (2FA) without even trying to intervene by investigating factors within individuals that may influence their decision to adopt 2FA by themselves.

A total of 1,852 individuals from all 34 provinces in Indonesia participated in this study by filling out online questionnaires. The authors discussed the results from statistical analysis further through the lens of the loss aversion theory.

The authors found that loss aversion, represented by higher income that translates to greater potential pain caused by losing things to be the most significant demographic factor behind 2FA adoption. On the contrary, those with a low-income background, even if they have some college degree, are more likely to skip 2FA despite their awareness of this technology. The authors also found that the older generation, particularly females, to be among the most vulnerable groups when it comes to authentication-based cyber threats as they are much less likely to adopt 2FA, or even to be aware of its existence in the first place.

Authentication is one of the most important topics in cybersecurity that is related to human-computer interaction. While 2FA increases the security level of authentication methods, it also requires extra efforts that can translate to some level of inconvenience on the user's end. By identifying the associated factors from the user's ends, a necessary intervention can be made so that more users are willing to jump on the 2FA adopters' train.

Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.

This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.

Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.

The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

Major public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.

To demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.

Through the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.

The authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

The description of “blockchain banking”, the determination of “the sub-processes” of “blockchain banking” as a “business process”, and the assessment of “maturity level” in Parsian Bank.

Theoretical sources on “blockchain banking” were initially investigated. Then the “sub-processes” of “blockchain banking” as a “business process” were extracted by Parsian Bank's experts through the “Delphi method”. Next, the “sequence” of the “sub-processes” was determined by means of the “AHP”. Eventually, Parsian Bank's maturity levels for all the sub-processes as well as the overall maturity level were specified on the basis of the “CMMI” V1.3 in order for Business Process Management (BPM).

Blockchain banking’ combines traditional banking with cryptocurrencies, which can be provided by merging “hybrid e-wallet” with “bank account” and “bank card” – all together as “crypto bank account”. Plus, “hybrid e-wallet” is a form of mobile e-wallet on blockchain that supports both cryptocurrencies and traditional currencies in the same platform by which the purchase and sale of cryptocurrencies are possible. Besides, “Blockchain banking service” can also be offered within the framework of “open banking” aligned with “open innovation” through a FinTech (or a beta bank) in collaboration with a licensed bank via “open API”, which is called “blockchain banking based on FinTech”. At last, the eight sub-processes of “blockchain banking” were determined and Parsian Bank's “maturity level” was specified.

This is the very first practical guide to “blockchain banking service”.