Exploring the multi-dimensionality of authenticity in dining experiences using online reviews

The quest for authenticity in dining experiences has become increasingly important. This paper explores authenticity dimensions that are of value to customers in dining experiences, and by that gains a multi-dimensional understanding of authenticity in this context. Following an integrated learning approach using text mining and classification techniques, this paper explores and confirms different dimensions of authenticity by identifying and classifying authenticity judgements in online restaurant reviews. The results suggest that authenticity is a multi-dimensional concept encompassing Authenticity of the Other, Authenticity of the Producer, and Authenticity of the Self as first-level dimensions. Additionally, besides historical and categorical authenticity which have been previously explored in the literature, a new type of authenticity - Deviated Authenticity - emerged as a second-level dimension falling under Authenticity of the Other. This paper enhances existing conceptualisations of authenticity and establishes avenues for exploring the multi-dimensionality of other consumer research concepts using user-generated content.     

External pressure or internal culture? An innovation diffusion theory account of small retail businesses’ social media use

COVID-19 put unprecedented external pressure on small businesses to adopt or increase use of social media while not all small businesses are internally ready for this rapid change. This study investigated the roles of external pressures and organizational culture of openness and learning in driving small retail business owners'/managers' social media use decisions by impacting their perceived usefulness and barriers, based on the innovation-decision process model from the diffusion of innovations theory and the theory of reasoned action. An online survey with structured measurements was administered to 411 U.S. small retail business owners/managers. Results from structural equation modeling revealed that external pressures positively influenced small business owners'/managers’ perceived usefulness and barriers and social media use intention. In addition, the culture of openness and learning positively influenced the perceived usefulness while mitigating the perceived barriers, thereby directly and indirectly influencing the social media use intention. Theoretical and managerial implications are discussed.     

Smart technology and service employees’ job crafting: Relationship between STARA awareness,performance pressure,receiving and giving help,and job crafting

Owing to the rapid spread of smart technology, artificial intelligence, robotics, and algorithms (STARA), service jobs are being replaced by these technologies. Despite the potential effect of STARA awareness on employee outcomes, surprisingly few studies have explored its role service employees' work outcomes. Addressing this gap, our research assessed the mediating relationship between STARA awareness, performance pressure, and job crafting, as well as the moderating effects of help-receiving and help-giving on this relationship. We conducted a quantitative study for 301 South Korean service employees using surveys collected at two points in time. The results indicated that service employees' STARA awareness positively affected job crafting through performance pressure. Furthermore, the association between STARA awareness and performance pressure was more prominent for higher levels of help-receiving than for lower levels of help-receiving. Help-receiving further moderated the indirect effect of performance pressure on the STARA awareness and job crafting relationship. While the mitigating effect of help-giving was not supported, we observed a significantly positive association between STARA awareness and performance pressure only when help-receiving was high and help-giving was low. These findings contribute to the literature on technology in the retailing sector by uncovering how STARA awareness affects service employees’ performance pressure and job crafting and the roles played by help-giving and help-receiving in this relationship.     

Multinational mining enterprises and artisanal small-scale miners: From confrontation to cooperation

In many developing countries, multinational enterprises (MNEs) in mining operate alongside fast-growing communities of informal artisanal, small-scale miners. This has led to direct conflict and competition for mineral resources. This paper introduces the Salience and Institutional Analysis and Design framework as a means to analyze the MNE strategies used to address informal miners across different governance levels in the gold mining sector of Ghana and discusses the implications of these strategies for sustainability. We identify the emergence of a cooperative strategy with informal miners as a sustainable alternative to the political strategy of reliance on the state to protect tenure.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

Video mining: Measuring visual information using automatic methods

Marketers are becoming increasingly reliant on videos to market their products and services. However, there is no standard set of measures of visual information that can be applied to large datasets. This paper proposes two standard measures that can be automatically obtained from videos: visual variation and video content. The paper tests the measures on crowdfunding videos from a leading online crowdfunding website, and shows that the proposed measures have explanatory power on the funding outcomes of the projects. These measures can be effectively implemented and used for large datasets. Further, researchers can apply these measures to other sets of visual information, and marketers could use the research to guide their video design and improve their video marketing effectiveness.     

Improving candy industry competitiveness: Retailers’ perception regarding customer satisfaction

This study aims to classify service quality elements in candy industry in order to determine their instrumentality to customer satisfaction. To address this purpose, a Fuzzy Kano questionnaire was distributed to 512 major retailers of candy products of four predominant candy companies in Iran. Subsequently, we calculated satisfaction increment index (SII) and dissatisfaction decrement index (DDI) for each of the quality elements. Brand name and quality are considered as must-be elements, while availability of credit purchases is considered as indifferent element by the majority of retailers. Eight out of 24 studied elements are found to be considered as attractive elements by majority of customers. Regular visits element ranks first in terms of satisfaction increment index (SII). This paper provides helpful insight for marketers to prioritize improvement of different quality elements. Therefore, this paper helps organizations to perceive their customers needs more thoroughly and improve their customer satisfaction policy.     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.     

Exchange market pressure and primary commodity – exporting emerging markets

This paper studies short-term sensitivity between exchange market pressure and various domestic and external factors in primary commodity-exporting emerging markets. The paper focuses on the top country-commodity groups in sugar, cereal, fuels, ores and coffee during the pre-peak and post-peak commodity price periods across floating and pegged exchange rate regimes, using the price of crude oil as a general benchmark. Employing a panel model and panel VAR analysis, the paper finds the heterogeneity of response patterns unique to country-commodity groups and exchange rate regimes. According to the results, in flexible regimes, volatility occurs via the foreign exchange market, interest rates, and domestic credit cycles, feeding into the social costs for structurally weaker economies. Hard exchange-rate pegs often result in a drain on international reserves as the terms of trade deteriorate following post-price peaks, leading to unpopular depreciation. These results accentuate concerns over uneven international trade patterns, an open economy’s short-term foreign exchange policy, and speculative capital flows. Such sensitivity has broad implications for macroeconomic balance and the sustainability of implied exchange rate targets in the presence of a foreign exchange constraint across emerging markets.     

Crowdsourcing-based semantic relation recognition for natural language questions over RDF data

ABSTRACT

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods.