Search results

This study examines the effect of contextual perceived value activated by contextual marketing offers and information privacy concerns on consumer loyalty in mobile commerce.

The survey was conducted through 340 mobile users in Morocco and the collected data were analyzed using structural equation modeling.

This study's results show that contextual marketing and information privacy concerns are key determinants in improving customer loyalty in the m-commerce context. Perceived ubiquity has a positive impact on perceived trust, which also impacts consumer loyalty. Information privacy concerns also have a positive impact on customer satisfaction, yet it does not impact perceived trust, which is contrary to the results of other researchers. It can also be concluded that customer satisfaction and trust are important antecedents of consumer loyalty.

This research gives rise to some important managerial and strategic implications in order to integrate contextual marketing strategies, as well as theoretical implications that concern this field of study.

This research makes a significant contribution to knowledge by examining the role of contextual marketing and information privacy concerns in the m-commerce context. These results will be considered useful for marketers and for businesses in general who wish to integrate a marketing strategy that is based on a customer-centric approach. It also contributes to the related literature, as there are few studies focused on m-commerce and contextual marketing within the context of Morocco.

This study aims to elucidate the process and internal mechanism of place identity construction in traditional villages under the impact of tourism by taking Cuandixia village as a case. The research methods comprise participatory observation and in-depth interviews with the residents. The main results are as follows: the impact of tourism on traditional villages is mainly reflected in space reconstruction, livelihood change, social relations restructuring and culture change; under the impact of tourism, the representation of residents’ identity construction shows complexity, with positive and negative effects; and the place identity construction of residents affects their perception of and attitudes toward tourism. Moreover, self-esteem and self-efficacy principles play a key role in their perception of tourism. This study provides some reference for further investigation of the tourism development model and the mental mechanism of residents in traditional villages.

Both technological and human-centric perspectives need to be acknowledged when combining lean production practices and Industry 4.0 (I4.0) technologies. This study aims to explore and explain how lean production practices and I4.0 technologies may coexist to enhance the human-centric perspective of manufacturing operations in the era of Industry 5.0 (I5.0).

The research approach is an explorative and longitudinal case study. The qualitative data collection encompasses respondents from different job functions and organizational levels to cover the entire organization. In total, 18 interviews with 19 interviewees and five focus groups with a total of 25 participants are included.

Identified challenges bring forth that manufacturing organizations must have the ability to see beyond lean production philosophy and I4.0 to meet the demand for a human-centric perspective in socially sustainable manufacturing in the era of Industry 5.0.

The study suggests that while lean production practices and I4.0 practices may be considered separately, they need to be integrated as complementary approaches. This underscores the complexity of managing simultaneous organizational changes and new digital initiatives.

The research presented illuminates the elusive phenomena comprising the combined aspects of a human-centric perspective, specifically bringing forth implications for the co-existence of lean production practices and I4.0 technologies, in the transformation towards I5.0.

The study contributes to new avenues of research within the field of socially sustainable manufacturing. The study provides an in-depth analysis of the human-centric perspective when transforming organizations towards Industry 5.0.

The outbreak of COVID-19 not only had serious negative impacts on the world economy but also on the global mental health because of the psychological disorders associated with the spread of the pandemic, the increased degree of uncertainty and the unprecedented measures taken by different countries to face the pandemic’s spread. This paper analyses the mental health well-being of individuals in selected MENA countries (Jordan, Morocco, Tunisia and Egypt) during the pandemic.

The study employs a pooled OLS model using the Economic Research Forum (ERF) COVID-19 MENA Monitor Survey panel dataset collected during 2020 and 2021.

The findings show that there is no association between the mental health of individuals in the selected countries and their age, gender, family size, marital status, receipt of social support and participation in care work. Mental health improved at higher levels of education, being employed, being a rural area resident and living in Morocco or Tunisia compared to living in Jordan while it worsened as income declined, food insecurity and anxiety about being infected with Covid-19 increased, being a resident in camps, and during waves 4 and 5. Based on these results, it is recommended that suitable financial, physical and human resources should be directed towards the provision of mental health care services in the region. Also, mental health care services should be accessible to different population groups, with a special focus towards the most vulnerable since they are more prone to mental illnesses, especially during health crises and economic shocks. This should be accompanied by increasing awareness about the provided services and reducing stigma against mental illnesses. Furthermore, introduction of policies targeted towards reducing food insecurity and income instability can play a key role in enhancing mental well-being.

Although few papers have previously investigated the impact of COVID-19 on mental health in MENA countries, most of them have focused on a country-level analysis and adopted a gender perspective. Hence, this paper aims at exploring the association between mental health well-being and socio-economic factors in selected MENA countries during the pandemic.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and carbon dioxide (CO2) emissions are mostly associated with food production. Balancing the trade-offs between energy intensity and food security remains a top priority for environmentalists. Despite the critical role of the environment in food security, there is a scarcity of substantial studies that explore the statistical connections among food security, CO2 emissions, energy intensity, foreign direct investment (FDI) and per capita income. Therefore, this study aims to provide more precise and consistent estimates of per capita CO2 emissions by considering the interplay of food security and energy intensity within the context of emerging economies.

To examine the long-term relationships between CO2 emissions, food security, energy efficiency, FDI and economic development in emerging economies, this study employs correlated panel-corrected standard error, regression with Newey–West standard error and regression with Driscoll–Kraay standard error models (XTSCC). The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies.

The study reveals that increasing food security in a developing economy has a substantial positive impact on both CO2 emissions and energy intensity. Each model, on average, demonstrates that a 1 percent improvement in food security results in a 32% increase in CO2 levels. Moreover, the data align with the Environmental Kuznets Curve (EKC) theory, as it indicates a positive correlation between gross domestic product (GDP) in developing nations and CO2 emissions. Finally, all experiments consistently demonstrate a robust correlation between the Food Security Index (FSI), energy intensity level (EIL) and exchange rate (EXR) in developing markets and CO2 emissions. This suggests that these factors significantly contribute to environmental performance in these countries.

This study introduces novelty by employing diverse techniques to uncover the mixed findings regarding the relationship between CO2 emissions and economic expansion. Additionally, it integrates energy intensity and food security into a new model. Moreover, the study contributes to the literature by advocating for a sustainable development goal (SDG)-oriented policy framework that considers all variables influencing economic growth.

Rosewood, as the most internationally traded endangered species, is subject to a series of restrictive trade policies globally. China has historically been the largest importer of rosewood in the world. The fluctuation of China’s rosewood import prices will have a profound impact on the global rosewood trade pattern. This study, therefore, assessed the impact of restrictive trade policies on China’s rosewood import prices to explore the fluctuation rule of rosewood trade prices under restrictive policies.

The study built a partial equilibrium framework about the formation mechanism of rosewood import price bubbles under supply constraints. On this basis, with China’s daily import prices of major rosewood species, the generalized supremum augmented Dickey–Fuller (GSADF) and backward supremum augmented Dickey–Fuller (BSADF) tests were applied to explore the effect of restrictive trade policies on China’s rosewood import prices.

The empirical analysis revealed that there were multiple price bubbles for five of the seven rosewood species. The largest bubbles were always created before and after the deployment of supply constraints. The empirical results for the counterfactual examples implied that price bubbles would not have occurred if restrictive rosewood trade policies had not been implemented. The above findings indicated that these measures tended to trigger significant price bubbles in China’s rosewood imports.

The effect of restrictive rosewood trade policies on rosewood trade prices had not yet been explored in previous research studies. This study empirically analyzed the effect of restrictive trade policies on China’s rosewood import prices using econometric models.

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

The authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.

Empirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.

Limited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.

The study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.