Search results

The online retail industry is enormous, covering a great assortment of products and services. Yet, little research has examined whether determinants of success in online retailing are similar or differ by industry sector. The purpose of this study is to examine industry sectors separately to distinguish drivers associated with overall satisfaction for the online consumers in those sectors.

The paper uses ridge regression to examine how e‐service quality dimensions are associated with overall customer satisfaction for several e‐retailing sectors.

While several e‐service quality dimensions exhibit a similar impact across all sectors, several other dimensions exhibited sector‐by‐sector differences. The drivers that frequently differ across sectors include price perceptions, ease of returns and refunds, and privacy experience.

As an exploratory study, research opportunities and limitations derive from the public source of data, sample sizes within some of the sectors, and the ridge regression methodology.

Related prior research examined individual e‐retailing sectors, leading to scattered sets of findings that cannot be triangulated. The research allows for comparison by the manager, and presents empirical insights for managers in several e‐retail sectors.

This paper is one of only a few studies to examine a consistent set of e‐service quality measures on a sector‐by‐sector basis. The paper is also unique in that it uses publicly available data sources to examine these relationships, providing managers with insights on how they might analyze such public data sets for their own usage.

Omnichannel consumers are more proactive, engage in longer shopping journeys and share their experiences. However, their postpurchase behavioral responses remain understudied. This paper aims to examine how a seamless omnichannel environment can contribute to a more optimal shopping experience (flow state) and the subsequent impact on the likelihood of generating mixed (positive and negative) word of mouth (WOM).

A controlled experiment was conducted with 220 participants to test the proposed model based on the stimulus–organism–response (S-O-R) model and flow theory. The authors conducted an analysis of variance, two regression analyses and two mediation analyses to test the hypotheses.

The results confirm a positive direct effect of a seamless environment on consumers' flow state and a positive (negative) direct impact of flow on the likelihood of generating positive (negative) WOM. Additionally, the results suggest that flow mediates the effect of a seamless environment on WOM.

This study contributes to omnichannel and WOM literature by exploring the critical role of seamlessness in consumers' subjective experience (flow state) and postpurchase behaviors (mixed WOM). In conjunction with the relevant theoretical contributions, these findings also offer guidelines for practitioners to manage the seamless environment and mixed WOM in the omnichannel context.

In much of the literature written in Sustainability and Environmental Justice, the focus is on the effects of government mismanagement or corporate social irresponsibility, or CSR ignored for the goal of greater profits. Certainly we have seen natural resources ripped from communities and nations for the benefit of corporate profits (Sarkar, 2013). The idea that a participatory government will lead to greater efforts for sustainability must be viewed in the light of its times and economy (Gonzalez-Perez, 2013). What happens when the man-made disaster precedes or clashes with natural disaster? The Great Recession of 2008 was stunning in the rapidity with which it spread around the globe. The recession illustrated a global acceptance of financial wisdom that had been presented as fact and yet could easily be undermined by people who understood the barriers, boundaries, and restrictions in place as well as where the financial assumptions could be deceived by introduction of new terms and definitions as in the case of credit default swaps.

In this chapter, we focus on the influence of the recession on one of the most powerful financial capitals of the world, New York City. We discuss the general effect of the recession on New York City as a whole and then take a narrower look at each of the five boroughs, Manhattan, Queens, Staten Island, Brooklyn, and the Bronx. We examine the disparate economic states of each borough and how the recession has impacted each of them. Furthermore, we discuss the implications of the general perception of Manhattan’s resilience to the recession and how is has impacted the other boroughs, such as the housing crisis in Staten Island and Queens following Hurricane Sandy, unemployment rates in the Bronx, and the rebuilding of a sustainable job market in Brooklyn.

We reviewed relevant literature, including academic research, reports issued by the State of New York, census data, articles printed in popular press outlets, and business resources to provide a thorough look at the influence of the Great Recession on New York City and each of its five boroughs. We found extensive support for the disparity amongst the five boroughs, despite the perception that New York City is thriving in the wake of the Great Recession and Hurricane Sandy. We detail the unique economic and environmental factors of each borough and explain how it influenced the impact of the Great Recession and subsequent natural disaster.

Manhattan was well insulated from the initial impact of the Great Recession, with tourism in the city remaining high through 2008 and financial firms on Wall Street experiencing record high profits well into 2009. Despite the downfall of Lehmann Brothers and Merrill Lynch, the financial bailouts and Federal Reserve credit available to Wall Street firms prevented Manhattan’s financial sector from experiencing the dramatic unemployment rates that the rest of New York and the United States were facing (DeFreitas, 2009).

The Great Recession hit disadvantaged areas, like the Bronx, harder than other areas of New York, while Hurricane Sandy halted the economic recovery in areas like Queens and Staten Island. While unemployment remains low in New York City as a whole, the recovery from the Great Recession has been uneven, further widening the gap between New York City’s boroughs, with the lower income areas at a greater disadvantage and the higher income areas souring. While Manhattan has recovered significantly, with Wall Street profits reaching record levels in 2009, other boroughs haven’t experienced the same economic upturn and are still facing significant challenges (Parrott, 2010). While the city has gained nearly 375,000 jobs, nearly twice the number of jobs that were lost during the Great Recession (Crain’s New York Business, 2013), the significant variance in wages and high costs of living has not greatly reduced the number of working poor across New York City and has not resulted in an evenly spread boost in wealth.

At the end of our chapter, we discuss “lessons learned” and, in particular, the importance of preparation for both fiscal and natural disasters. Local policy makers must ensure that the needs of its constituents are being met and will be met in the future if such hardship were to strike. Government leaders need to have a forward-looking plan, rather than simply handling immediate needs.

The originality of our content stems from a deeper look into the nuances of the economy of New York City. Statistics paint a picture of a thriving City, despite the Great Recession. However, understanding the distinct differences amongst the five boroughs illustrates that these citywide averages do not paint an accurate picture of life for New Yorkers off of Wall Street. The extent to which the high-income areas in Manhattan have recovered suggests that the economy of New York City as a whole is thriving, whereas the reality is that the middle-class has not recovered and the previously disadvantaged are now even more so. It is important to look at each of the five boroughs of New York City individually when creating policy to both recover from and prevent events such as the Great Recession and the destruction of Hurricane Sandy. Our chapter illustrates stark differences within New York City in the face of both financial and natural crises.