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The purpose of the current study is to help remove the obstacles to sustainable production and consumption by revealing the determinants of green consumption behaviour, which is one of the precursors of sustainable economic growth. This study aims to expand the theory of planned behaviour (TPB) model and contribute to the relevant literature by investigating the factors of social media usage, social media marketing and digital marketing interactions that have not been investigated before in relation to green product purchasing behaviour.

This study examines the effect of the extended TPB model on consumers’ intention to buy green products in Turkey, which has a Middle Eastern culture and is a developing economy. In the study, data collected from 409 participants with the questionnaire method were analysed using SmartPLS 4.0 and IBM SPSS 26 statistical programs.

The study findings revealed that all the structural elements of TPB (attitude, subjective norms, personal behaviour controls) and social media marketing and digital marketing interactions contribute to consumers’ green product purchasing behaviour. The study findings also demonstrated that the use of social media is not effective in the purchasing of green products.

Understanding consumers’ perspective on purchasing green products is crucial for policymakers, businesses and marketers, as it helps formulate appropriate strategies to support sustainable economic growth. In this respect, this study has important implications for sustainable consumption and production. In addition, to the best of the authors’ knowledge, the study is the first to examine consumers’ green product purchasing behaviour in the context of sustainable economy.

Artificial intelligence (AI) is shaping the future of the marketing world. This study is the first to examine the effect of AI marketing efforts, brand experience (BE) and brand preference (BP) in light of the stimulus-organism-response (SOR) model.

The data collected from 398 participants by the questionnaire method were analyzed by SEM (structural equation modeling) using Smart PLS 4.0 and IBM SPSS 26 programs.

We find that four SOR elements of AI marketing efforts (information, interactivity, accessibility and personalization) positively impact bank customer BE, BP and repurchase intention (RPI). Further, we find that BE plays a mediator role in the relationship between AI marketing efforts, RPI and BP.

The findings of the study have significant implications for the bank marketing literature and the banking industry, given the limited evidence to date regarding AI marketing efforts and bank–customer relationships. Moreover, the study makes important contributions to the AI marketing and brand literature and helps banks increase customer experience with artificial intelligence activities and create long-term relationships with customers.

The purpose of this paper is to introduce a novel cross (+) type yoke with hybrid electromagnets and new reluctance modeling to precisely calculate attraction force is given.

The comparison of attraction force and torque analyses between the proposed formulation and the existing formulation in the literature is comparatively presented. For the correctness of the force and torque values calculated in the model created, the system was created in ANSYS Maxwell and its accuracy was proved by making analyses. The maglev carrier system is inherently unstable from the point of view of control engineering. For that, it needs an active controller to eliminate this instability. For the levitation of the carrier system, it is necessary to design a controller in three axes (z, α and β). I-PD controller was designed for the air gap control of the carrier system in three axes and the controller parameters were determined by the canonical method.

While the new formulation proposed in the modeling of the carrier system has a maximum error of 1.03%, the existing formula in the literature has an error of 16.83% in the levitation distance point.

A novel cross-type hybrid carrier system has been proposed in the literature. With the double integral used in modeling the system, it takes a long time to solve symbolically, and it is difficult to simulate dynamic behavior in control validation. To solve this problem, attraction force and inclination torque values are easily characterized by new formulation and besides the simulations are conducted easily. The experimental setup was manufactured and assembled, and the carrier system was successfully levitated, and reference tracking was performed without overshoot.

This study aims to examine whether adding a quantity scarcity message to a monetary discount helps to improve consumers’ offer-related perceptions and intentions, and how the effectiveness of that message compares with adding time restriction to the offer.

Two experiments, where participants evaluated retail ads and responded to relevant measures, were conducted in two country markets.

Adding either a quantity scarcity message or time restriction to a monetary discount increases the potency of a retail offer. Further, when an offer ad emphasizes product and price-related cues in a balanced manner, time restriction results in more favorable consumer perceptions than scarcity. However, this difference in the messages’ efficacy disappears when the offer strongly emphasizes price-related cues.

The US market sample is more homogeneous than the Indian one. Discounts were presented in terms of advertised reference prices; further research with other discount formats is desirable.

Understanding the relative efficacy of quantity scarcity message and time restriction in discounted retail offers can give managers flexibility in the use of these tools.

This paper addresses scholars’ call for theory-grounded research that provides guidance to retailers on the use of sales promotional tools.

This study aims to focus on the information sharing behavior of employees in the context of online brand advocacy based on the cognition–affection–behavior framework.

Online surveys were distributed to 840 employees, and the gathered data was analyzed by using structural equation modeling.

The effects of perceived organizational justice, employee emotions, organizational trust on online brand advocacy and the moderating role of organizational identification were empirically supported in this study. Noteworthy exceptions to these findings included the lack of evidence of the effect of informational justice on emotions, the effect of procedural justice on negative emotion and the effect of negative emotion on online brand advocacy.

This study expands brand activism research by investigating online brand advocacy and the employee context. Moreover, this work also extends online brand advocacy research through the employee points of view.

The purpose of this paper is to investigate the effect of doping element on the microwave absorption performance of hexagonal nano boron nitride (h-nBN)-reinforced basalt fabric (BF)/epoxy composites. A new type of hybrid composite that will be produced by the use of boron nitride as an additive that leads to increased radar absorption capability will be developed and a new material that can be used in aeronautical radar applications.

This study is focused on the microwave absorption properties of h-nBN doped basalt fabric-reinforced epoxy composites. Basalt fabric (BF)/epoxy composites (pure composites) and the BF/h-nBN (1 Wt.% h-nBN doped composites) hybrid composites were fabricated by vacuum infusion method. Phase identification of the composites were performed using X-ray diffraction (XRD), the 2θ scan range was from 10 to 60 with the scanning speed of 3°/min and surface morphologies of the composites were investigated using scanning electron microscopy (SEM). Microwave properties of samples were investigated through transmission/reflection measurements in Agilent brand 2-Port PNA-L Network Analyzer in the frequency range of 3–18 GHz. The prepared sample is positioned between two horn antennas with and without metal plate.

Experimental results show that h-nBN doped composite was synthesized successfully and the produced hexagonal nano boron nitride-added fiber laminated composite material has good absorption behavior when they are used with metallic sheets and good for isolation applications at many points in the 3–18 GHz band.

This paper will contribute to the literature on the use of basalt fabric, which are new types of fibers, and hexagonal nano boron nitride and the effects of boron nitride on radar absorption properties of composite material. It presents detail characterization of each composite by using XRD and scanning electron microscopy.

This study aims to identify the relationship between individual differences of customers and their intention to use Near Field Communication (NFC)-based mobile payment (MP) technology in restaurants.

Amazon’s Mechanical Turk (MTurk) was used to collect the data of this study. A brief demonstration video of a NFC-MP system on how to use the technology was included at the beginning of the survey to make sure that all respondents had a sufficient understanding of the technology. A total of 410 valid online questionnaires were collected. ANOVA with Dunnett’s C post hoc analysis was used to compare the groups’ mean differences of gender, age, prior experience, income and education.

The findings indicated that there are significant differences in customers’ intention to use NFC technology in restaurants for past experience, age and gender. However, unlike previous studies’ statements, education and income did not play a significant role in restaurant customers’ NFC MP adoption behavioral intention.

This study investigated the restaurant customers’ NFC “intention to use” and did not observe the restaurant consumers’ actual behavior. A longitudinal study measuring actual behavior would be more beneficial to support the result of this study. Likewise, this study did not include the potential differences that might exist between various restaurant segments and ownerships (e.g. franchise vs chain or fast food vs fine dining). Accordingly, future studies should take this factor into account and replicate the study to detect any potential differences in restaurant customers’ behavioral intention.

Segmenting the market according to customers’ demographics could be a competitive advantage for the company. Based on the findings of this study, technology vendors and restaurant operators can define and design more effective strategies that are compatible with various market segments to increase the adoption of NFC-MP technology.

Food and travel experts believe that mobile payment will soon become the preferred method of payment in near future. Moreover, many restaurants in the USA are currently utilizing NFC-based MP systems because of its benefits. However, recent market research shows that NFC-based MP is still not popular among restaurant customers, as its rate of adoption is low. As little research has been conducted on MP in general and on consumers’ NFC-based MP adoption behavior in particular, a better understanding of individual differences influencing the adoption of mobile payments should be acquired.

近场通讯（NFC）技术在餐饮业中的应用逐渐流行。然而, 消费者对于这种技术的使用率却仍然很低。本论文研究个体区别对餐饮业中NFC移动技术使用的影响。

本论文通过亚马逊Mturk收集数据。问卷开始, 先播放有关NFC移动支付系统的使用短视频, 以使受访者对该技术有足够的了解。样本数量共为410份在线问卷。本论文使用Dunnett’s C post hoc ANOVA分析方法来对比个体区别, 包括性别、年龄、之前经历、收入、和学历。

本论文研究表明, 消费者的过去经历、年龄、和性别, 对消费者在餐馆使用NFC技术有显著影响。然而, 与之前文献结论相悖, 本论文未发现学历和收入对消费者在餐馆中使用NFC移动支付行为有显著影响。

本论文研究了餐馆顾客对NFC技术的使用倾向, 而并未观察到餐馆顾客的真实使用行为。长期研究衡量实际使用行为将对本论文结果有很好的支持作用。相同的, 本论文并没有研究对不同餐饮形态和经营权的潜在差异做区分（比如, 专营制vs 连锁制 或者快餐类 vs高档餐厅）。因此, 未来研究可以参考这些因素, 重复本研究设计, 以发掘餐馆顾客在行为意向中的潜在区别因素。

根据消费者人口统计指数来实行细分市场战略可以成为企业增加竞争力的一种方式。本研究表明, 科技提供商和餐馆经营者应该定义和制定更有效的战略, 使得其满足不同的细分市场需求, 增加NFC移动支付技术的使用。

餐饮和旅游专业认为, 移动支付将很快成为消费者倾向使用的支付方式。此外, 美国很多餐馆看到其有利, 正在使用NFC移动支付系统。然而, 近期营销研究显示, NFC移动支付的使用率还是很低, 并没有得到餐馆顾客的青睐。既然总体上很少研究对移动支付进行了研究, 尤其是NFC基础的移动支付技术, 那么针对个人特色对移动支付的使用的研究显得必要。

近场通讯（NFC）、移动支付、餐馆、技术使用、消费者行为

文献综述

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

Machine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews.

Well-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process.

BDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation.

To the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.

Accelerated share repurchases (ASRs) represent an important recent innovation in repurchase methods. Although executives often mention signaling undervaluation as a motivation for ASRs, managing earnings per share (EPS) has been argued as a key alternative motivation in the financial press. This paper aims to investigate whether ASRs are driven by managerial opportunism (i.e. managing EPS) or managerial optimism (i.e. signaling undervaluation) and whether stock market participants see through these motives.

The sample consists of 293 ASRs conducted between 2004 and 2011. Firms suspected of using ASRs to manage EPS (EPS-suspect firms) were identified by examining actual reported EPS, as-if EPS (i.e. EPS that would have been reported in the absence of an ASR), and analysts’ consensus EPS forecasts. A logistic regression of EPS-suspect versus non-EPS-suspect ASR transactions was performed. Analysts’ reactions to ASR announcements and investors’ reactions to post-ASR earnings announcements were examined. Changes in post-ASR operating performance were also analyzed.

Twenty-nine per cent of ASR firms (EPS-suspect firms) would have missed the consensus EPS forecasts had they not implemented the repurchase. Managerial incentives – securing bonuses and maintaining reputation by avoiding EPS misses – appear to lie behind this opportunistic use of ASRs. Upward revision observed in analysts’ EPS forecasts upon the announcement of ASRs is short-lived, indirectly facilitating firms’ use of ASRs to meet or beat consensus forecasts. Investors, however, are not fooled by managers’ use of ASRs as an earnings management device. Unlike EPS-suspect firms, non-EPS-suspect firms exhibit positive abnormal operating performance during the post-ASR period, suggesting that these firms use ASRs as a signaling device rather than as an earnings management device.

ASRs can be used by managers to signal better future performance to investors. However, managers who intend to do so should carefully consider the timing of an ASR. Initiating an ASR when the company is facing the risk of missing analysts’ EPS forecasts may be interpreted as the ASR being motivated by EPS management concerns rather than signaling, diminishing the credibility of a positive signal intended to be conveyed through the ASR. Further, when considering payout policy and executive compensation decisions, corporate boards need to be cognizant of managers’ incentives for undertaking ASRs. The use of ASRs opportunistically to boost EPS is prevalent, and this action is followed by poor performance.

A number of novel results are documented using tests that are methodologically distinct from those used in related previous research. Notably, this is the first study to distinguish between EPS-suspect and non-EPS-suspect ASR firms and examine the determinants as well as consequences of using ASRs as an earnings management versus signaling device. One out of every four ASR firms are EPS-suspects. Analysts react to ASR announcements by only temporarily increasing their short-term EPS forecasts. Investors see through managers’ use of ASRs as an earnings management device. While ASRs are prone to managerial opportunism, a large number of firms use ASRs to communicate favorable information about their future operating performance to investors.