1.a) How does technology change traditional marketing? Describe the important Internet properties that affect marketing. b) What are the key elements of Web 2.0? c) What are the key elements of Web 3.0? As a consumer who is in control, what would you like to see for the future Web 3.0?
2.a) Is strategic planning and why do companies prepare a SWOT analysis during the strategic planning process?
What are the four levels of commitment to e-business? Give
some examples of each.
b) What are metrics and why are they important? Give examples of metrics in use for a) social media awareness/exposure metrics b) social media brand health metrics c) social media engagement metrics d) social media action metrics and e) social media innovation metrics.
3.a) What are the seven steps in an e-marketing plan? Explain each step in details.
b) What four elements in tier one and five elements in tier two are devised for e-marketing strategy? Explain them in detail.
Beja and Goldman (1980) rightfully kingdom that a market constructed with the aid of human beings can impossibly be so automatically ideal and green that each one facts would without delay be incorporated within the charges earlier than it may be observed. Implying that price anomalies will always be present, leaving room for predictability. furthermore, Pesaran (2003) reinforces predictability through pointing out that “A massive quantity of studies within the finance literature have confirmed that stock returns can be anticipated to a few degree by means of interest prices, dividend yields and an expansion of macroeconomic variables displaying clean commercial enterprise cycle versions.” consistent with Pesaran market performance ought to be distanced from predictability. method statistics series & Processing most of the facts and queries used for the studies had been received thru Wharton university of Pennsylvania’s WRDS database & question device (Wharton studies facts offerings). on this studies, three exclusive datasets are used that exist inside the WRDS database, named: CRSP – every day stock, IBES – charge target and Federal Reserve bank – hobby charges. those sub-datasets in the end could be merged earlier than the hypotheses may be examined and may be elaborated on within the following segment. in addition details on the datasets may be received from desk A1 in which all question extraction specifications are denoted. the chosen information duration from 1999 to 2017 is a change-off between covering a length as huge as possible while on the identical time seeking to maintain the records editable within Stata the use of the restrained computing electricity that the studies has to its disposal. furthermore, considering IBES data is simplest to be had from 1999 onwards, this will routinely be the start of the period. moreover, it can be argued the use of Glantz & Kissel’s (2013, p. 258) parent 1 that algorithmic trading earlier than 1999 could have amounted to such a small percent of the market quantity that it is not of crucial cost in answering the research query. moreover, simplest NASDAQ and NYSE equity fee data is used as the U.S. based totally inventory exchanges had been first in setting up centers to help the improvement of algorithmic trading. therefore, excessive frequency buying and selling received extent proportion within the US greater unexpectedly than in Europe, as proven in discern 5 (Kaya, 2016, p. 2). Given those arguments and thinking about the confined computing power, U.S. statistics on algorithmic buying and selling follows as the extra hooked up preference. parent five. % percentage of high Frequency buying and selling in general equity buying and selling in step with yr. Reprinted from “high-frequency trading: accomplishing the limits.” through O. Kaya, 2016, 2. Copyright by way of Deutsche bank research. CRSP – day by day inventory to begin with, the each day fees and trading information along with the every day number of trades and day by day extent are extracted from the CRSP U.S. inventory database inside WRDS. The preceding cited CRSP query will function as the master dataset in the Stata surroundings and incorporates quit-of-day expenses for fairness securities at the NYSE and NASDAQ exchanges. moreover, CRSP additionally contains quote statistics, retaining length returns, stocks first-rate and buying and selling volume informati>GET ANSWER