Search results

This study aims to both identify content-based and interaction-based online consumer complaint types and predict complaint types according to the complaint magnitude rooted in complainants' personality traits, emotion, Twitter usage activity, as well as complaint's sentiment polarity, and interaction rate.

In total, 297,000 complaint tweets were collected from Twitter, featuring over 220,000 consumer profiles and over 24 million user tweets. The obtained data were analyzed via two-step machine learning approach.

This study proposes a set of content and profile features that can be employed for determining complaint types and reveals the relationship between content features, profile features and online complaint type.

This study proposes a novel model for identifying types of online complaints, offering a set of content and profile features that can be used for predicting complaint type, and therefore introduces a flexible approach for enhancing online complaint management.

This study aims to provide a contemporary, holistic and systematic review regarding the impact of country-of-origin on industrial purchase decision-making by ascertaining, synthesizing and evaluating theoretical, methodological and empirical dimensions of studies on the subject.

After the collection and refinement of 43 studies that appeared in marketing, business and management literatures during 1970-2017, systematic review was applied to discover the current situation and future research directions on the subject.

The vast majority of the existing studies obtained data from a single source even though industrial purchase decisions are mainly made by a large group of decision-makers. Moreover, the existing literature contains few over-studied theoretical perspectives while lacking integration of the subject with the more contemporary ones. Additionally, in the literature, combination of developing countries as source countries, and developed ones as target countries, is under-examined, and the differentiation between country-of-design and country-of-assembly is mainly avoided during operationalization. Finally, majority of the studies lack investigation of antecedents and mainly investigated few over-examined constructs as outcomes.

This study provides a conceptual synthesis of existing studies. A meta-analysis may be applied to empirical studies for providing a further detailed framework.

This study contributes to industrial marketing literature by providing a compiled and synthesized inventory of knowledge for scholars; deriving a comprehensive analysis of research designs, methodologies and findings addressed by researchers in the field; noticing various theoretical, methodological and other gaps to be examined; and providing future research directions.

This study aims to empirically investigate the linkages between digital trails of social signals (content and profile features of bitcoin-related tweets) and bitcoin price return using a VAR-BEKK-GARCH model.

Bitcoin-related tweets were collected every hour for six months from September 1, 2020, to February 29, 2021. The analysis involved two steps: first, examining tweet content, profiles, sentiment and emotions; and second, investigating the relationship between social signal volatility and hourly bitcoin price return.

Results indicate that bitcoin price changes can impact the sentiment expressed in tweets about bitcoin, and vice versa. While sadness exhibits a bidirectional volatility spillover with bitcoin, fear and anger display a one-period lag. Quartile analyses reveal that only fear in the second quartile shows a bidirectional spillover effect with bitcoin, while all other emotions except sadness demonstrate a unidirectional spillover effect in all remaining quartiles.

The study uses a novel two-step approach to analyze volatility spillovers between social signals and bitcoin price returns. Findings can guide investors and portfolio managers in making better allocation decisions and assist policymakers and regulators in reducing the adverse effects of bitcoin’s volatility on financial system stability.

This paper aims to provide a comprehensive and systematic review of the extant empirical body of knowledge regarding the impact of Guanxi on international Business-to-Business (B-to-B) relationships.

After the collection and refinement of studies that appeared in marketing, business and management literature during 1995-2018 period, a systematic review was conducted to discover the current situation and future research directions on the subject.

Theoretically, vast majority of the reviewed studies lacked a theoretical foundation, with the remainder anchored primarily on the resource-based view, social network theory and social exchange theory. Methodologically, Ganqing, Xinren and Mianzi are the most frequently investigated dimensions, whereas Renqing is the least investigated dimension. Data are mostly obtained from both Chinese and Western counterparts through survey and analyzed through univariate and multivariate data analysis techniques. Empirically, extant research focused on many diverse outcomes including trust, financial performance, cooperation, satisfaction, time orientation, opportunism and liability of foreignness, while under-examining the drives of Guanxi.

This study provides a synthesis of extant line of research on the subject that are published in peer-reviewed international journals, which publish research in English. A meta-analysis may be conducted for providing a further detailed framework.

This study contributes to international marketing literature by providing an in-depth and synthesized inventory of knowledge to scholars; deriving a comprehensive analysis of theoretical foundations, methodological approaches and findings addressed by scholars in the field; noticing theoretical, methodological and empirical gaps to be examined; and providing future research directions.