netrashetty
Netra Shetty
Entergy Corporation is an integrated energy company engaged primarily in electric power production and retail distribution operations. It is headquartered in the Central Business District of New Orleans, Louisiana.
information can then been exploited in order to identify the types of adjustments that need
to be made to correct for that bias.
3.1 Issues in Survey Methodology
The literature on the contingent valuation survey method, where people are asked to state
their willingness-to-pay for a good, relates most closely to my survey method. Several critiques have been made about the validity of the contingent valuation method (c.f. Hausman
1993) as well as of the entire Öeld of survey data. However, in several ways my survey either escapes those critiques or employs methods of responding to those critiques which were
suggested in the literature.
One problem with contingent valuation surveys is that respondents must often estimate
the value of a vaguely deÖned item for which they have no previous market or pricing
experience (e.g., ìHow much do you value clean water?î). In my case, this criticism is
not as relevant; the presence of a retail market helps to create realistic bounds for my
participantsívaluations. Empirical studies comparing contingent valuation surveys to actual
revealed preference data show that the estimates correspond very closely to the market
prices. (Bjornstad & Kahn 1996) In addition, my survey respondents see everything that
the bidder sees in that particular auction, so my survey reáects the appropriate informational
context. The literature often refers to the di§erential e§ects that starting points can make
in valuations. (Aadland & Caplan 1999; Bateman & Willis 1999) However,both the actual
auction participants and my respondents would be ináuenced by the same types of anchor
prices appearing in auction descriptions or in the retail market.
Another criticism of survey data is the lack of realistic incentives. Respondentsívaluations
may be ináated because they are not dealing with their own money, and have no incentive
to be conservative. On the other hand, their valuations may be deáated since they have
no incentive to think carefully about their true maximum willingness-to-pay. As a result,
respondents may have di§erent dispersion of valuations than the actual bidders. This is an
important criticism, and I suggest a procedure for correcting for such potential di§erences
between bidders and respondents in Section 6.
4 Survey Performance
On average, I collected 46 responses per auction. For each respondent i and for each auction
t, I denote the respondentís valuation of the item by Xi;t
I denote the average of the .
responses in each auction by Vt
I denote the standard deviation of responses in each auction .
by SDt
: Summary statistics are presented in Table 1.
In Figure 1, auctions are ordered along the horizontal axis by increasing eBay price, Pt
.
The corresponding averages of survey responses, Vt
, are plotted for each auction as well.
The plot shows that Vt
is highly correlated with Pt
If prices are correlated with the value .
of the item, then this plot suggests that Vt
is likely to be correlated with the value of the
The latest eCustomerServiceIndex (eCSI) results from eDigitalResearch and IMRG in January 2011 show that consumers are using digital devices on the high street to give store feedback, check stock levels and check that they are getting the best deals.
The emergence of MEcommerce has seen the consumer taking control and having the power to shop when they want, where they want and how they want. The latest study shows that consumers are using phones and PCs as well as the high street and now expect a seamless shopping experience from brands across all channels.
The study has also shown that 25% of smartphone users have been in a high street store and used a barcode scanner on their smartphone to scan items within the store and see if they can order cheaper elsewhere.
This illustrates that the high street stores need to ensure that they differentiate this channel of shopping by offering excellent customer service and ensuring stock is available and queues are kept to a minimum.
40% of people surveyed had given feedback digitally about a high street store after seeing a survey URL in a store or on a receipt, of those people 88% gave their feedback via a PC whereas only 7% have utilised their smartphones to give immediate feedback.
High street stores and their staff are also starting to embrace the digital link up with staff using digital devices to check stock levels for consumers.
The study has shown that 39% of people surveyed have had staff in a high street use a smartphone, tablet, PC or kiosk to check stock levels for them.
Derek Eccleston, Research Director at eDigitalResearch, comments,
'We are seeing the dawn of MEcommerce with digital now truly coming to the high street with consumers using technology to provide feedback and check prices with suppliers whilst the staff within stores are utilising the digital technology to offer the consumer excellent customer service by checking stock levels’.
David Smith, Managing Director at IMRG, said,
‘Developments in mobile-commerce have led to the phenomenon of the ever-connected consumer, who can access multiple retailer channels concurrently, while instantly comparing and contrasting with similar offerings on competitor sites. Although only 7% have provided immediate feedback to the store, people do update their friends on Facebook about being stuck in a long queue, receiving negative customer service or poor hygiene conditions. The need for high standards is more important than ever, as any individual retailer store is now a potential showroom for millions.’
information can then been exploited in order to identify the types of adjustments that need
to be made to correct for that bias.
3.1 Issues in Survey Methodology
The literature on the contingent valuation survey method, where people are asked to state
their willingness-to-pay for a good, relates most closely to my survey method. Several critiques have been made about the validity of the contingent valuation method (c.f. Hausman
1993) as well as of the entire Öeld of survey data. However, in several ways my survey either escapes those critiques or employs methods of responding to those critiques which were
suggested in the literature.
One problem with contingent valuation surveys is that respondents must often estimate
the value of a vaguely deÖned item for which they have no previous market or pricing
experience (e.g., ìHow much do you value clean water?î). In my case, this criticism is
not as relevant; the presence of a retail market helps to create realistic bounds for my
participantsívaluations. Empirical studies comparing contingent valuation surveys to actual
revealed preference data show that the estimates correspond very closely to the market
prices. (Bjornstad & Kahn 1996) In addition, my survey respondents see everything that
the bidder sees in that particular auction, so my survey reáects the appropriate informational
context. The literature often refers to the di§erential e§ects that starting points can make
in valuations. (Aadland & Caplan 1999; Bateman & Willis 1999) However,both the actual
auction participants and my respondents would be ináuenced by the same types of anchor
prices appearing in auction descriptions or in the retail market.
Another criticism of survey data is the lack of realistic incentives. Respondentsívaluations
may be ináated because they are not dealing with their own money, and have no incentive
to be conservative. On the other hand, their valuations may be deáated since they have
no incentive to think carefully about their true maximum willingness-to-pay. As a result,
respondents may have di§erent dispersion of valuations than the actual bidders. This is an
important criticism, and I suggest a procedure for correcting for such potential di§erences
between bidders and respondents in Section 6.
4 Survey Performance
On average, I collected 46 responses per auction. For each respondent i and for each auction
t, I denote the respondentís valuation of the item by Xi;t
I denote the average of the .
responses in each auction by Vt
I denote the standard deviation of responses in each auction .
by SDt
: Summary statistics are presented in Table 1.
In Figure 1, auctions are ordered along the horizontal axis by increasing eBay price, Pt
.
The corresponding averages of survey responses, Vt
, are plotted for each auction as well.
The plot shows that Vt
is highly correlated with Pt
If prices are correlated with the value .
of the item, then this plot suggests that Vt
is likely to be correlated with the value of the
The latest eCustomerServiceIndex (eCSI) results from eDigitalResearch and IMRG in January 2011 show that consumers are using digital devices on the high street to give store feedback, check stock levels and check that they are getting the best deals.
The emergence of MEcommerce has seen the consumer taking control and having the power to shop when they want, where they want and how they want. The latest study shows that consumers are using phones and PCs as well as the high street and now expect a seamless shopping experience from brands across all channels.
The study has also shown that 25% of smartphone users have been in a high street store and used a barcode scanner on their smartphone to scan items within the store and see if they can order cheaper elsewhere.
This illustrates that the high street stores need to ensure that they differentiate this channel of shopping by offering excellent customer service and ensuring stock is available and queues are kept to a minimum.
40% of people surveyed had given feedback digitally about a high street store after seeing a survey URL in a store or on a receipt, of those people 88% gave their feedback via a PC whereas only 7% have utilised their smartphones to give immediate feedback.
High street stores and their staff are also starting to embrace the digital link up with staff using digital devices to check stock levels for consumers.
The study has shown that 39% of people surveyed have had staff in a high street use a smartphone, tablet, PC or kiosk to check stock levels for them.
Derek Eccleston, Research Director at eDigitalResearch, comments,
'We are seeing the dawn of MEcommerce with digital now truly coming to the high street with consumers using technology to provide feedback and check prices with suppliers whilst the staff within stores are utilising the digital technology to offer the consumer excellent customer service by checking stock levels’.
David Smith, Managing Director at IMRG, said,
‘Developments in mobile-commerce have led to the phenomenon of the ever-connected consumer, who can access multiple retailer channels concurrently, while instantly comparing and contrasting with similar offerings on competitor sites. Although only 7% have provided immediate feedback to the store, people do update their friends on Facebook about being stuck in a long queue, receiving negative customer service or poor hygiene conditions. The need for high standards is more important than ever, as any individual retailer store is now a potential showroom for millions.’
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