Four decades of negative word-of-mouth and negative electronic word-of-mouth: A morphological analysis

Customers voice their negative brand experiences to their family and friends in the form of negative word-of-mouth (NWOM). Web 2.0 enables the sharing of NWOM in electronic format on various social media sites, online customer review forums, and blogs, which is known as negative electronic word-of-mouth (NeWOM). Researchers need to study the spread of NWOM/NeWOM to prevent adverse consequences for companies and suggest an optimal response for its redressal. Existing literature review studies have focused on word-of-mouth (WOM) and electronic WOM (eWOM) and have considered both positive and negative WOM and eWOM concurrently. Past literature reviews have captured the breadth of the WOM domain, ignoring the depth. This research article contains a review of 282 journal papers capturing the depth of the extant literature by focusing solely on ‘negative’ WOM and eWOM (NWOM and NeWOM), and presents a broad view of the NWOM and NeWOM domains using morphological analysis (MA). This will help to conceptualize and categorize the existing state-of-the-art literature into broad dimensions and identify future research opportunities. The MA framework helps to bifurcate this literature into the following four dimensions: (i) nomenclature of NWOM and NeWOM, (ii) antecedents of NWOM/NeWOM, (iii) impacts of NWOM/NeWOM, and (iv) prevention and recovery response to NeWOM. Further dissection of these four dimensions leads to 15 sub-dimensions and 217 variants. Combinations of the 217 variants enable the identification of 550 novel future research opportunities in the area of NWOM and NeWOM.     

Did the 2016 election cause changes in substance use? An intersectional approach

This paper examines the impact of the 2016 election on substance use. One of Donald Trump's major campaign initiatives was to build a wall at the American/Mexican border, and he frequently made negative comments about various underrepresented groups. I hypothesize that this unorthodox rhetoric, coupled with Donald Trump's proposed policies during his campaign, created an exogenous shock of discrimination for women, and ethnic and racial minority groups when he was elected. The Behavioral Risk Factor Surveillance System dataset, which has a variety of health measures and demographic information compiled at the individual level, is utilized to empirically examine the hypothesis. I examine the average treatment effects of the election using a difference-in-differences estimation strategy. The results indicate that there has been an increase in cigarette use for Hispanic individuals. These results highlight the impact that discriminatory rhetoric by political leaders and public figures has on marginalized groups within society. Policy implications include new ways to target substance use by targeting discrimination.     

Ride the trend: Is there spread momentum profit in the US commodity markets?

Some previous researchers have argued that trading strategies based on calendar spread time series momentum (STSM) can deliver significant returns (Szymanowska et al. 2014; Boons and Prado 2019), which, if true, is at odds with the efficient market hypothesis. These arguments however, do not exclude the unrealisable futures contract roll yield and are also affected by other empirical and statistical issues that may lead to misleading results. With more than 30 years of data, we investigate STSM in 22 US commodity futures markets. First, we assess whether past spread returns can predict future returns, a necessary condition for the existence of momentum. We find predictability to be very weak after correcting for the issues affecting prior research. Second, we implement STSM-based investment strategies. We compare STSM profits for individual markets and portfolios to profits generated by a simple long-only benchmark strategy that does not require any predictability. STSM does not generate returns statistically different from the benchmark trading strategy, with both strategies generating very low or negative returns. For the momentum to outperform the benchmark strategy, predictability should be three times larger than observed from real data, but would entail substantial downside risk. In sum, the empirical evidence indicates that returns from STSM-type strategies are illusive for the commodities and period studied. Our results strongly suggest that inclusion of unrealisable roll yield generates the illusion of profitable STSM trading strategies in previous research.