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The study aims to look at deep eWOM providing behaviour in m-commerce and attempts to explore its antecedents. Personalisation is proposed as an indirect antecedent of deep eWOM providing behaviour mediated by hedonic and utilitarian value perceptions and personal identification.

Based on social-exchange theory, the conceptual model links the study antecedents to deep eWOM providing behaviour. The conceptual model was validated through a multi-country study. A large sample of m-commerce users in the UK (n = 505), India (n = 422) and Vietnam (n = 618) were contacted to collect the data. Data were analysed through structural equations modelling procedure with invariance analysis conducted to ensure that the results from the three samples could be compared. The authors also conducted post-hoc analysis to explore the mediation paths between variables.

The study finds support to the conceptual model across the samples from the three countries. Personalisation is found to increase value perceptions – both utilitarian and hedonic – and personal identification which leads to “deep” eWOM providing behaviour across all the three countries. The serial mediation also provides comparable results across the three countries.

The study contributes to the understanding of deep eWOM providing behaviour – a construct with high practical relevance which has however not been explored sufficiently in current literature. The study also contributes to the literature that analyses the consequences of personalisation in m-commerce.

Brands are increasingly considering the use of chatbots to supplement, or even replace, humans in service interactions. Like humans, chatbots can follow certain service scripts in their encounters, which can subsequently determine the customer experience. Service scripts are verbal prescriptions that seek to standardize customer service interactions. However, while the role of service scripts is well documented, despite the increasing use of chatbots as a service mechanism, less is known about the effect, on consumers, of different service scripts presented during chatbot service encounters.

An experimental scenario was developed to test the research hypotheses. Respondents were randomly allocated to scenarios representing a 2 (service interaction: human, chatbot) × 2 (service script: education, entertainment) design. A total of 262 US consumers constituted the final sample for the study.

The findings indicate that when employing an education script, a significant positive effect occurs for human service agents (compared to chatbots) in terms of both satisfaction and purchase intention. These effects are fully mediated by emotion and rapport, showing that the bonds developed through the close proximity to a human service agent elicit emotion and develop rapport, which in turn influence service outcomes. However, this result is present only when an educational script is used.

This paper contributes to the emerging service marketing literature on the use of digital services, in particular chatbots, in service interactions. We show that differences occur in key outcomes dependent on the type of service script employed (education or entertainment). For managers, this study indicates that chatbot interactions can be tailored (in script delivered) in order to maximize emotion and rapport and subsequently consumer purchase intention and satisfaction.

The purpose of this paper is to examine how the risk of war aroused by North Korea’s threatening actions trigger strategic responses from US multinational enterprises (MNEs) operating in South Korea. The authors compare two competing perspectives of real options and risk diversification to see which prevails when US MNEs are facing risk of war.

The authors hand collected news articles regarding North Korea’s threatening actions that may trigger strategic responses from MNEs operating in South Korea. The authors use archival data of US MNEs to verify our results.

Empirical tests of the two competing perspectives reveal that US MNEs adopt the risk diversification strategy when threatened by the risk of war. However, as MNEs have more available foreign markets outside the host country that is at risk of war, MNEs tend to take an operational flexibility approach more seriously and shift their productions to the remaining global operations. The ownership structure of the subsidiary does not appear to have significant effect on US MNEs’ strategic risk management.

This paper compares two perspectives, namely, real options and risk diversification, to observe how US MNEs treat their subsidiaries when facing risk of war in South Korea.

This paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).

The method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.

Paired t-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.

Results demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.

The technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.

This paper aims to test whether passive Wi-Fi visitor analytics is a useful and effective method to measure consumer engagement towards food trucks located within an outdoor activation area at an Australian metropolitan university.

Using passive Wi-Fi visitor analytics to ping and track smart devices, data was collected over 90 weekdays capturing data from 522,548 unique smart devices.

The data collected in this feasibility study was able to identify the most and least popular food trucks by displaying the differences in both bounce and engagement rates, suggesting that passive Wi-Fi visitor analytics are feasible and useful in this context. Furthermore, the results also demonstrate that food truck vendors and marketers should not engage in random rotation, but instead remain static to try and increase familiarity.

Current visitor tracking technology (i.e. ticketed sales, sales data and survey) is limited as it may not provide an accurate measurement of foot traffic, identify engaged patrons who passed by but did not complete a purchase and be available due to commercial sensitivity and confidentiality. Thus, the current research is the first to examine customer engagement (i.e. unengaged walk-by vs engaged but bounced vs engaged sales) with food trucks within an activation area by using passive Wi-Fi visitor analytics.

当前的论文旨在研究被动 Wi-Fi 访客分析是否是衡量消费者对位于澳大利亚城市大学户外活动区域内的流动餐车的参与度的有用且有效的方法。

使用被动 Wi-Fi 访客分析来跟踪智能设备, 从 522,548 个独特的智能设备收集了超过 90 个工作日的数据。

该可行性研究中收集的数据能够通过显示跳出率和参与率的差异来识别最受欢迎和最不受欢迎的流动餐车, 这表明被动 Wi-Fi 访客分析在这种情况下是可行和有用的。 此外, 我们的结果还表明, 流动餐车供应商和营销人员不应随意轮换, 而应保持静止从而增加顾客熟悉度。

当前的访客跟踪技术（即售票销售、销售数据和调查）是有限的, 因为它可能无法：（1）提供客流量的准确测量; (2) 识别路过但未完成购买的参与顾客; (3) 由于商业敏感性和保密性而可用。 因此, 目前的研究是第一个通过使用被动 Wi-Fi 访客分析来检查激活区域内流动餐车的客户参与度（即, 未参与路过, 相比于参与但跳出, 相比于参与售出额）。