Search results

Switching behavior is predominantly seen in the consumer buying behavior of the mobile industry. This research aims to identify the factors influencing consumers to switch from their present mobile service provider. The consumer of the mobile industry operates in a dynamic and ever-changing environment that is difficult to predict, so this paper aims to focus on these issues.

The selection of factors was made with the help of qualitative study and quantitative research methods for further findings; with the help of a structured questionnaire, a total of 514 valuable responses were collected to get the results. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to analyze the data.

The finding shows that technology and edge-on-competition (TEC) and pricing have a negative influence on customer switching behavior. The switching cost (SC) is the most significant factor and has a positive impact, while service encounter failure (SEF) also positively impacts switching behavior.

The findings provide important implications for consumers switching brands if they are finding alternative offers that are cost-effective and SEF from service providers

The study of one of the largest mobile markets is learning lessons for other markets around the world. This study will be helpful for mobile service provider companies in their branding and marketing strategies. This study will also be helpful to practitioners, educators and researchers in understanding the consumer behavior of mobile users.

The learning of the largest mobile market will be a great learning lesson for other mobile markets around the world. Consumer behavior will help marketers follow ethical practices and make their strategy so a consumer does not switch brands and remain satisfied with the existing brand.

The study provides unique learning for practitioners, educators and researchers to understand the consumer behavior of mobile users. This will help marketers create factors that stop consumers from switching brands and develop strategies to retain customers.

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

This paper examines the national solidarity in Brunei Darussalam during the COVID-19 pandemic and its consequential impact on younger generations. Utilising Emile Durkheim's solidarity theories, I examine how young people's social media use builds on state discourse in the pandemic. I contend that a shift towards an organic society is visible through a social cohesion that is based on differentiated roles. I argue that the citizenry plays a vital role in the forward momentum toward Industrial Revolution (IR) 4.0, which illustrates that solidarity cannot be forged as a top-down directive. By prompting economic and creative divisions of labour, the local use of social media in a public health crisis has shown the government a new way to foster solidarity. Significant implications for youth as future leaders of the nation are discussed.

Cognitive dysfunctions are a common clinical feature of schizophrenia and represent important indicators of outcome among patients who are affected. Therefore, a randomized, controlled, monocentric, single-blind trial was carried out to compare two different rehabilitation strategies adopted for the restoration and recovery of cognitive functioning of residential patients with schizophrenia. A sample of 110 residential patients were selected and, during the experimental period, a group of 55 patients was treated with sets of domain-specific exercises (SRT+CRT), whereas an equal control group was treated with sets of non-domain-specific exercises (SRT+PBO) belonging to the Cogpack® software. The effects on the scores (between T0 and T1) of the variables treatment and time and of the interaction time X treatment were analyzed: for the total BACS, the main effect of the between-factors variable treatment is statistically significant (F=201.562 P=0.000), as well as the effect of the within-factors variable “time” (F=496.68 P=0.000).The interaction of these two factors is also statistically significant (F=299.594 P=0.000). The addition of cognitive remediation therapy (CRT) to a standard treatment of metacognitive training (MCT) resulted in a significant improvement in global neurocognitive functioning and has reported positive effects with regard to the strengthening of verbal and working memory, selective and sustained attention at T1. A relevant result is the statistically significance of “time X treatment” for all the tests administered: we can assume that the domain-specific cognitive training amplifies the effects of SRT, as the primary and secondary goals of the present study were achieved.

Using the combined theoretical umbrella of organizational legitimacy theory, service-dominant logic, fairness heuristic theory and two-factor theory, the purpose of this paper is to investigate the effectiveness of different food recall strategies (recall proactiveness and compensation) in terms of both how consumers react (perceived organizational legitimacy and purchase intention) and how recall norms would influence the effectiveness in three countries. In addition to the reporting of important results, this paper provides implications for food companies to handle effectively the recalls, especially when the recalls are cross-country.

A 2 compensation (high vs low) ×2 recall strategy (proactive vs passive) scenario experiment was conducted in Hong Kong, the USA and Mainland China. After checking the effectiveness of manipulation, the paper tested the main effect and interaction effect of recall proactiveness and compensation on perceived organizational legitimacy and purchase intention. In addition, the mediating effect of perceived organizational legitimacy between recall strategies and purchase intention was also tested.

Significant main effect, interaction and mediation effect were found across the three countries with a different pattern. For the USA and Mainland China which have strong recall norms, the interaction found followed the predictions of the two-factory theory. However, the pattern found in Hong Kong, which has weak recall norms, followed the predictions of the fairness heuristic theory. Full mediation effect of perceived organizational legitimacy between compensation and purchase intention was found in the USA and Mainland China, while it was only partial in Hong Kong. For the mediation between proactiveness and purchase intention, full mediation was found in Hong Kong and the USA, while it was only partial in Mainland China.

First, this study differentiated food recall strategy into two dimensions – recall proactiveness and compensation. Second, this study tested the applicability of two-factor theory and fairness heuristic theory in recalls by testing the competing hypotheses proposed according to the two theories. Finally, this study can further help our understanding of the recall effectiveness across different recall norms.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.