Smart Digital Marketing Capabilities for Sustainable Property Development: A Case of Malaysia

Factors Influencing the Use of Digital Marketing by Small and Medium-Sized Enterprises during COVID-19

This study aims to identify the factors that influence the use of digital marketing by SMEs in Medellín during COVID-19, proposing five factors that influence the use of these digital tools by 120 SMEs in Medellín, Colombia. The research was carried out under an exploratory factorial analysis, with a quantitative approach and an exploratory-descriptive scope. For data analysis, the levels of association between the constructs of the conceptual model and the intention to use these virtual tools were estimated, using Cramer’s V coefficient. The results identify the benefits perceived by customers, the perceived advantages of using digital tools, and business optimization as the key factors in predicting their acceptance and use. It is concluded that the most used digital marketing strategies are the content and web sites of Instagram and Facebook. As the main implications, the study contributes to understanding the behavior of companies regarding technological change which could help to identify needs and successful strategies that ensure the continuity and sustainability of this business sector. As limitations, the hypotheses were tested in a single context, so it is necessary to compare these results in developed countries to contribute to a global approach. In addition, only marketing professionals who held operational positions were surveyed, so future managers and leaders of the marketing area should be included in the studies.

Innovative Knowledge Automation Framework in DM and Collaborative Edge Computing Social IoT Systems

Digital marketing-based innovative knowledge management helps people inspire creativity and cultural changes required to advance the organization and satisfy changing business requirements. Knowledge workers can respond more rapidly when they have quicker access to resources and information across the company. A knowledge-based approach views innovation as a process characterized by the knowledge needed to understand how the innovation was created. The term “digital marketing automation” (DMA) refers to software platforms and technologies built for marketing departments and enterprises to sell online and automate tedious tasks more effectively. Digital marketing encompasses all forms of advertising that take place online, including but not limited to websites, search engines, social media, email, and mobile apps. An entirely new approach to big data processing has emerged because of the rise of edge computing in the internet of things environment. As a result of these findings, a distributed neural network cloud-edge computing paradigm is presented.

A Bibliometric Analysis and Systematic Review on E-Marketplaces, Open Innovation, and Sustainability

In recent years, the rise of e-commerce has prompted the emergence of electronic marketplaces, or e-marketplaces, which act as intermediaries in the buying and selling process, bringing together several vendors to offer a wide range of products and services to customers, generating modalities such as business-to-business (B2B), business-to-consumer (B2C) or consumer-to-consumer (C2C) e-marketplaces. E-marketplaces offer advantages such as access to potential buyers, business and product visibility, the reduction of transaction costs, the comparison of offers and prices among competitors, and the ease of business internationalization. However, the success of e-marketplace business models depends on the sustainability of these platforms, which must involve different stakeholders in order to meet economic, environmental, and social objectives. Therefore, this study presents a bibliometric analysis and systematic review of e-marketplaces, open innovation, and sustainability for the last ten, five, and two years. The analysis includes the number, types, and subject areas of documents published each year, as well as considerations such as the most-cited publications and the leading authors, journals, countries, and institutional affiliations. The analysis also includes a study of the relevant concepts in the publications and their relationships, identifying the predominant topics related to e-marketplaces, open innovation, and sustainability. The results indicate a focus on subject areas such as social sciences, environmental sciences, energy, business, management, and accounting, which is consistent with the economic, environmental, and social dimensions of sustainability. The findings show that e-marketplaces, open innovation, and sustainability are closely related to concepts such as sustainable development, e-commerce, digital marketing, China (the leading country in terms of publications in all periods), logistics, supply chain management, big data, planning, and decision making. Future works should address traffic congestion and environmental impact, new delivery practices in last-mile logistics, and the motives for users’ engagement in e-marketplaces. Likewise, future research can be oriented toward sustainability dimensions and stakeholders’ integration through open innovation and toward the limitations of SMEs in order to access and benefit from digital platforms.

Automatic Target Detection from Satellite Imagery Using Machine Learning

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 × 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

The Impact of Digital Marketing Success on Customer Loyalty

Customer loyalty is often seen as one of the decisive factors in determining the fate of organizations. In the age of digitization, organizations should think of advanced strategies to increase their competitiveness and market share by employing the potential of digital content and enhancing their digital capabilities. Recently, many digital media have appeared available to organizations to build their brand, reach their target customers and retain them, and promote their products. Digital media represented a real revolution that made it difficult for organizations to survive in the business environment without investing in these means. Therefore, the aim of the study is twofold: 1) to explore the effects of digital content marketing and digital marketing capabilities on digital marketing success; 2) to investigate the impact of digital marketing success on customer loyalty. This research was conducted on a sample of online restaurant customers through a questionnaire distributed to 187 customers. IBM SPSS 25.0 and AMOS 22.0 were used as the main programs for analysing the collected data from 165 online restaurant customers. Structural equation modelling was applied to test the research’s hypothesized effects. The results indicated that digital content marketing and digital marketing capabilities had significant effects on digital marketing success based on the perspectives of online restaurant customers in Jordan. Moreover, digital marketing success substantially impacted customer loyalty of online restaurant customers in Jordan. It was concluded that for organizations to ensure a positive effect of digital marketing on customer loyalty, more attention should be paid to critical factors such as digital content and digital capabilities. The study calls for researchers to explore more factors affecting the success of digital marketing. It informs marketing managers that customer loyalty in the digitization era depends on the ability to create, communicate and deliver digital content using digital technologies and the quality of digital content and capabilities that deliver customer value.

Investigating the Personalization–Privacy Paradox in Internet of Things (IoT) Based on Dual-Factor Theory: Moderating Effects of Type of IoT Service and User Value

Despite people’s concerns over privacy leakage in the Internet of Things (IoT), the needs for personalized IoT services are increasing, creating a conflicting phenomenon viewed as the personalization–privacy (P–P) paradox. This study proposes a research model that utilizes dual-factor theory to investigate the P–P paradox in IoT. It aims to analyze the impact of the dual factor—personalization and privacy concerns related to IoT services—on the intention to use IoT. Further, the model includes four-dimensional motivated innovativeness and previous privacy-invasion experience as key antecedents of the dual factor. Particularly, this study examines the moderating effects of the type of IoT service and user value on the relationship between dual factor and usage intention. Data were collected using a web-based survey. The results showed that personalization had a significant impact on the intention to use IoT, whereas privacy concerns did not. The effects of all antecedents except social innovativeness were significant. The P–P paradox phenomenon appeared differently depending on the type of IoT service and user value. This study contributes to gaining a better understanding of the factors that influence the increase in IoT usage in terms of both protecting and appropriately using personal information for IoT services.

Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.

Understanding and Acceptance of Smart City Policies: Practitioners’ Perspectives on the Malaysian Smart City Framework

Whilst a plethora of research exists on the smart cities and project performance evaluations, only few studies have focused on the smart city policy evaluation from the perspective of its acceptance by practitioners. This paper aims to generate insights by evaluating the smart city policy through a developing country case study—i.e., Malaysia. This study employed a questionnaire survey method for data collection and analyzed the data by using Fuzzy Delphi analysis. A group of 40 practitioners was gathered in a focus group discussion through purposive sampling. The main objectives of this survey were to identify the understanding and acceptance levels of the seven smart city domains and respective strategies that are outlined in the Malaysian Smart City Framework. The results disclosed that the practitioners possessed divergent levels of understanding and acceptance in terms of smart city domains. The study participant practitioners accepted all understanding and acceptance objectives of smart economy, living, people, and governance domains (expert agreement 75–92% and threshold d value 0.123–0.188), but rejected all objectives for both smart environment and digital infrastructure domains (expert agreement 55–74% and threshold d value 0.150–0.212). Along with this, acceptance of smart mobility was also rejected (expert agreement 56% and threshold d value 0.245). The findings reveal that considering all opinions expressing dissensus is essential when building more inclusive smart city strategies. This study contributes to the smart city discourse as being one of the first in capturing professional practitioners’ understanding and acceptance on a national level smart city policy by applying the Delphi method in the smart city context. Most importantly, the study informs urban policymakers on how to capture the voices and perspectives of the general public on national and local smart city strategy and initiatives.

UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.

Digital Transformation of Marketing Strategies during a Pandemic: Evidence from an Emerging Economy during COVID-19

This study explores the relationship between digital marketing practices, customer satisfaction, customer involvement, and purchase intention. The focus is on the life insurance digital marketing strategies during a pandemic and the resultant lockdown and shutdown. This work sought to analyze the digital transformation of marketing practices and the customers’ resultant purchase intentions. COVID-19 was taken as the prevailing pandemic and its impact on the digital transformation of marketing strategies. Five dimensions of digital marketing strategies with eighteen items and three items each of customer satisfaction and purchase intention were considered for practical purposes. It used structural equation modeling to study 535 responses of life insurance customers. Findings indicate that SEM/SEO, display, and E-CRM practices significantly impacted customer satisfaction and purchase intention. Further, a mediation-cum-moderation approach was undertaken. Customer satisfaction significantly affected purchase intention and played a good mediator between digital marketing practices and purchase intention. Additionally, customer involvement moderated the relationship between content marketing and communication with purchase intention. This research work helps life insurance marketers in general. The digital channel managers expressly understand their key areas of strengths regarding the five dimensions of digital marketing strategies. Accordingly, they frame their plans for decision-making to improve customer satisfaction and resultant purchase intentions. It provides a direction for future adoption of specific marketing strategies during a pandemic and consequent shutdown and lockdowns.

A Framework for Developing Green Building Rating Tools Based on Pakistan’s Local Context

Most countries have developed green building rating tools that are based on social, environmental, and economic dimensions. Pakistan followed a similar approach and has developed a rating tool known as Sustainability in Energy and Environmental Development (SEED). However, SEED is built on developed western countries’ rating tool standards which do not address Pakistan’s unique local context, especially from the cultural and governmental perspectives. This research aims to fill this research gap by developing a holistic framework of building rating tools that incorporates cultural and governmental dimensions. Based on an extensive literature review, a hypothetical framework, incorporating Pakistan’s unique local contexts and adding cultural and governmental dimensions to the widely adopted social, environmental, and economic dimensions of sustainability, was proposed in this paper. This framework was further validated by in-depth interviews with multiple stakeholders in Pakistan. A qualitative analysis of the interview results was carried out, and the final framework was proposed with key indicators, reflecting all five dimensions of sustainability. The verified sustainability framework can be used to improve or develop green building rating tools for Pakistan, and it can also inform other developing countries’ rating tool development.

Insight into the Impact of COVID-19 on Australian Transportation Sector: An Economic and Community-Based Perspective

The Coronavirus Disease 2019 (COVID-19) is a major virus outbreak of the 21st century. The Australian government and local authorities introduced some drastic strategies and policies to control the outspread of this virus. The policies related to lockdown, quarantine, social distancing, shut down of educational institute, work from home, and international and interstate travel bans significantly affect the lifestyle of citizens and, thus, influence their activity patterns. The transport system is, thus, severely affected due to the COVID-19 related restrictions. This paper analyses how the transport system is impacted because of the policies adopted by the Australian government for the containment of the COVID-19. Three main components of the transport sector are studied. These are air travel, public transport, and freight transport. Various official sources of data such as the official website of the Australian government, Google mobility trends, Apple Mobility trends, and Moovit were consulted along with recently published research articles on COVID-19 and its impacts. The secondary sources of data include databases, web articles, and interviews that were conducted with the stakeholders of transport sectors in Australia to analyse the relationship between COVID-19 prevention measures and the transport system. The results of this study showed reduced demand for transport with the adoption of COVID-19 prevention measures. Declines in revenues in the air, freight, and public transport sectors of the transport industry are also reported. The survey shows that transport sector in Australia is facing a serious financial downfall as the use of public transport has dropped by 80%, a 31.5% drop in revenues earned by International airlines in Australia has been predicted, and a 9.5% reduction in the freight transport by water is expected. The recovery of the transport sector to the pre-pandemic state is only possible with the relaxation of COVID-19 containment policies and financial support by the government.

Ambidextrous Marketing Capabilities, Exploratory and Exploitative Market-Based Innovation, and Innovation Performance: An Empirical Study on China’s Manufacturing Sector

Despite the acknowledged importance of innovation for enterprises’ sustainable development, in comparison to technological innovation, research on market-based innovation is still in its nascent stage. It is unclear what factors will predict market-based innovation and how market-based innovation boosts innovation performance. Therefore, based on an adaptive marketing capabilities perspective and drawing on insights from the organizational ambidexterity literature, this study explored the relationships among enterprises’ marketing capabilities, market-based innovation and innovation performance. Data collected from 237 manufacturing enterprises in China were analyzed using hierarchical regression analysis and bootstrapping methods. The results showed that: (1) Marketing capabilities and market-based innovation activities are both ambidextrous. Specifically, marketing capabilities include marketing exploration capabilities and marketing exploitation capabilities, and market-based innovation includes exploratory market-based innovation and exploitative market-based innovation. (2) Ambidextrous marketing capabilities can enhance innovation performance significantly, with ambidextrous market-based innovation playing a partial mediating role in this relationship. Specifically, marketing exploration capabilities affect innovation performance through the partial mediating role of exploratory market-based innovation, whereas marketing exploitation capabilities affect innovation performance through the partial mediating role of exploitative market-based innovation. (3) Exploratory market-based innovation negatively moderates the relationship between exploitative market-based innovation and innovation performance, and exploitative market-based innovation also exerts a negative moderating effect on the exploratory market-based innovation–innovation performance association. These findings complement and enrich the ambidextrous capability (innovation) theory, helping manufacturing enterprises to better understand and deploy ambidexterity, and hence to achieve sustainability.

Green and Sustainable Public Procurement—An Instrument for Nudging Consumer Behavior. A Case Study on Romanian Green Public Agriculture across Different Sectors of Activity

Green Public Procurement (GPP) became an efficient instrument to achieve the objectives of environmental policy expressed by the European Commission in its Communications. At the same time, it must be addressed by the public authorities as a complex process, in which all purchased goods and services must integrate perfectly into an entire puzzle-like system of legislation, the construction field, innovation, healthcare, food, and education. Scientific references published in the Web of Science (WoS) mainly between 2017 and 2020 were investigated, and they analyze the implications of green public procurement in various fields, as presented by scientific communities. This article brings as a novelty in this context the identification of some barriers in the adoption of these processes, so that they can be overcome. Based on good practices and international standards and trends, the article shows how aspects related to the implementation of green procurement in society can be taken into account. In the second stage, we added a case study on Romanian green agriculture and discussions regarding inter-correlation between different fields and GPP.