Kimberly Thomas-Francois, WooMi Jo, Simon Somogyi, Qianya Li and Andrew Nixon
Virtual grocery shopping (VGS), or online grocery shopping, traditionally has seen slow adoption in Canada; however, the COVID-19 pandemic has forced consumers to seek safer ways…
Abstract
Purpose
Virtual grocery shopping (VGS), or online grocery shopping, traditionally has seen slow adoption in Canada; however, the COVID-19 pandemic has forced consumers to seek safer ways to shop. Retailers have invested in building new infrastructure to meet the current consumer demands for VGS. However, the main driver for VGS behaviour has not been yet clearly identified. Additionally, it is also not unknown whether the shopping modalities will continue VGS after the pandemic. This study provides insights into consumer intentions to use VGS by extending the model of goal-directed behaviour (MGB) to incorporate consumer technological readiness.
Design/methodology/approach
The study collected 935 valid survey responses from an online survey panel of Canadian consumers. A two-step approach was applied to analyse the data, comprising confirmatory factor analysis (CFA) and structural equation modelling (SEM). The data quality and model fit were tested before testing the proposed relationships among the constructs: attitude, subjective norm, perceived behavioural control, positive and negative anticipated emotions, as well as technological readiness, desire and behaviour intentions. The mediation role of desire between frequency of past online grocery shopping behaviours and the future behavioural intention was also tested using SPSS PROCESS.
Findings
The study results showed that attitudes, subjective norms, perceived behavioural control, positive and negative emotions, technological readiness and frequency of past VGS have a major impact on consumers' desire to embrace VGS in the future. Consumer desire also played a significant mediating role between frequency of past VGS behaviours and future shopping intention online. In addition, the frequency of past VGS showed an even stronger impact on behavioural intention among female consumers than among male consumers.
Originality/value
The findings of this study provide an original insight into the social, cultural and psychological factors that impact consumers' use of VGS, particularly the impacts of gender.
Details
Keywords
Ya Qian, Wolfgang Härdle and Cathy Yi-Hsuan Chen
Interdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting…
Abstract
Purpose
Interdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting structure among industries. One of the reasons is that the interdependencies show a different pattern in tail events. This paper aims to investigate industry interdependency with the tail events.
Design/methodology/approach
General predictive model of Rapach et al. (2016) is extended to an interdependency model via least absolute shrinkage and selection operator quantile regression and network analysis. A dynamic network approach was applied on the Fama–French industry portfolios to study the time-varying interdependencies.
Findings
A denser network with heterogeneous central industries is found in tail cases. Significant interdependency varieties across time are shown under dynamic network analysis. Market volatility is identified as an influential factor of industry connectedness as well as clustering tendency under both normal and tail cases. Moreover, combining dynamic network with prediction direction information into out-of-sample industry return forecasting, a lower tail case is obtained, which gives the most accurate prediction of one-month forward returns. Finally, the Sharpe ratio criterion prefers high-centrality portfolios when tail risks are considered.
Originality/value
This study examines the industry portfolio interactions under the framework of network analysis and also takes into consideration tail risks. The combination of economic interpretation and statistical methodology helps in having a clear investigation of industry interdependency. Moreover, a new trading strategy based on network centrality seems profitable in our data sample.