The impact of artificial intelligence on sustainable corporate brand:a netnography study of tesla

Abstract. The global market has become ever more turbulent due to digitalisation and digital transformation. Artificial Intelligence (AI) plays a central role in moving forward the advance of technology. AI has become an important research field in marketing while various companies have successfully implemented AI technologies to meet customers’ needs. However, the impacts of AI on brands have not been widely explored in both scientific and managerial aspects. Brands generate values for businesses by providing functional and non-functional benefits that can be contributed by implementing AI technologies. Mainly, developing sustainability is crucial to address stakeholders’ concerns for today’s brands. The sustainable corporate brand can be a solution to this market demand as its promise has sustainability as a core value. Through exploring this phenomenon, the thesis answers the research question: to what extent does AI contribute positive impacts on sustainable corporate brands in the electric autonomous vehicle (EAVs) sector? The EAVs industry, represented by the case company, Tesla, is chosen for conducting this research because it integrates the variants of electric vehicles that provide environmental benefits and the autonomous cars that use AI technologies. The study is performed using the qualitative research method of netnography. The data are collected from the publicly available information on Twitter and Youtube based on their relevance to the research question. One hundred sixty tweets and thirteen Youtube videos are extracted in textual form and analysed following the guidelines of thematic analysis and triangulated with multiple sources of data. The key results of the research suggest the unique characteristics of the three AI features, machine learning, natural language processing (NLP) and Big Data analytics, help create the normative emotions and efficacy in the mind of stakeholders. These norms of emotions and efficacy further motivate stakeholders’ normative actions that, in return, enhance the normative emotions and efficacy in a loop. Five elements represent the values AI technologies contribute to brand promise through creating a unique experience for the stakeholders that differentiate the brand from its competitors. The refreshed excitements and trust are brought by machine learning technologies. The fun and human characteristics and safety are brought by NLP technologies. Technology superiority is made possible through Big Data analytics. Four elements act for the values conveyed by AI technologies that enrich and expand the brand identity. NLP features can effectively enhance the connections between the focal brand and the other brand associations: the CEO, the affiliate brands and meaningful cultural references. The shared ownership of the brand is intensified through the co-creation of Big Data analytics. By contributing to brand promise and brand identity, AI implementation helps foster positive impacts in building an authentic, emotionally charged, and behaviourally based sustainable corporate brand

EXPLORING E-LEARNING BEHAVIOR THROUGH LEARNING DISCOURSES

As many studies predict e-learning behaviors through intention, few of them investigate user’s learning behaviors directly. In addition to intention, individual’s e-learning behaviors may be influenced by technology readiness and group influences, such as social identity and social bond. This research-in-progress study explores how e-learning behaviors vary with intention, technology readiness, social identity and social bond. Our investigation was based on analyzing the speech acts embedded in fourteen learners’ online discourses in an eighteen-week e-learning course. We then compared how speech acts varied among groups with different degree of intention, technology readiness, social identity, and social bond. Our findings contribute e-learning research by clarifying how intention, technology readiness, social identity, and social bond influence learning behaviors in e-learning context

The Effects of Green Energy Production on Farmland: A Case Study in Yunlin County, Taiwan

Taiwan enacted the Act of Renewable Energy in the year 2009 which promotes energy safety, green economy, and a sustainable environment, and with that the government envisages a contribution of photovoltaic energy of up to 20% by the year 2025. In this study we look into the motivation and background of this energy policy, plans for implementation and associated challenges, and its actual consequences for farmland use and farmers. In addition, we take a look into the implementation of mixed-use farmland in which agricultural activity and photovoltaic installations are planned to coexist in order to increase land value and productivity. We furthermore report on some of our findings related to a field survey conducted in Taiwan’s corn chamber of Yunlin County which has been facing a number of socioeconomic challenges

Polynomial Fuzzy Observer-Based Feedback Control for Nonlinear Hyperbolic PDEs Systems

This article explores the observer-based feedback control problem for a nonlinear hyperbolic partial differential equations (PDEs) system. Initially, the polynomial fuzzy hyperbolic PDEs (PFHPDEs) model is established through the utilization of the fuzzy identification approach, derived from the nonlinear hyperbolic PDEs model. Various types of state estimation and controller design problems for the polynomial fuzzy PDEs system are discussed concerning the state estimation problem. To investigate the relaxed stability problem, Euler’s homogeneous theorem, Lyapunov–Krasovskii functional with polynomial matrices (LKFPM), and the sum-of-squares (SOSs) approach are adopted. The exponential stabilization condition is formulated in terms of the spatial-derivative-SOSs (SD-SOSs). Additionally, a segmental algorithm is developed to find the feasible solution for the SD-SOS condition. Finally, a hyperbolic PDEs system and several numerical examples are provided to illustrate the validity and effectiveness of the proposed results

Ring Chromosome 7 Presenting with Intrauterine Growth Restriction and Multiple Anomalies

SummaryObjectiveRing chromosome 7 is a very rare chromosomal anomaly that may have a grave prognosis. Nevertheless, the clinical features associated with ring chromosome 7 are highly variable. Here, we report a case with ring chromosome 7 and the perinatal findings.Case ReportA 32-year-old, gravida 1, para 0, woman was referred to our hospital because of intrauterine growth restriction (IUGR) and oligohydramnios at 35 weeks of gestation. Prenatal ultrasound revealed a severe IUGR fetus presenting with multicystic kidney, hydronephrosis and oligohydramnios. At parturition, the birth weight of this male infant was 1,720 g, and a battery of anomalies were also noted, including imperforate anus, hypospadia, micropenis, right cryptorchidism, severe IUGR, multiple nevi on the forehead, shoulder and left thigh, brain atrophy, right multicystic kidney, and left mild hydronephrosis. Cytogenetic study from cord blood revealed a ring chromosome 7.ConclusionRing chromosome 7 is extremely rare and our case might be the 15th and youngest case in the medical literature. Our case had multicystic kidney and imperforate anus, which have not been reported previously. Prenatal diagnosis of ring chromosome 7 is very difficult. When fetuses present with severe IUGR, oligohydramnios and multicystic kidney, chromosomal aberrations should be kept in mind, and perinatal cytogenetic workup is warranted

Pre-Emptive Treatment of Lidocaine Attenuates Neuropathic Pain and Reduces Pain-Related Biochemical Markers in the Rat Cuneate Nucleus in Median Nerve Chronic Constriction Injury Model

This study investigates the effects of lidocaine pre-emptive treatment on neuropathic pain behavior, injury discharges of nerves, neuropeptide Y (NPY) and c-Fos expression in the cuneate nucleus (CN) after median nerve chronic constriction injury (CCI). Behavior tests demonstrated that the pre-emptive lidocaine treatment dose dependently delayed and attenuated the development of mechanical allodynia within a 28-day period. Electrophysiological recording was used to examine the changes in injury discharges of the nerves. An increase in frequency of injury discharges was observed and peaked at postelectrical stimulation stage in the presaline group, which was suppressed by lidocaine pre-emptive treatment in a dose-dependent manner. Lidocaine pretreatment also reduced the number of injury-induced NPY-like immunoreactive (NPY-LI) fibers and c-Fos-LI neurons within the CN in a dose-dependent manner. Furthermore, the mean number of c-Fos-LI neurons in the CN was significantly correlated to the NPY reduction level and the sign of mechanical allodynia following CCI