本研究的主旨在探討負面消費者的評論如何影響消費者對產品的態度產生。負面意 見的力量通常會大過正面意見。本篇研究沒有考慮交換的問題。正面意見產生的積極 影響和負面意見產生的批評，兩者之間的權衡影響會是一個有趣的問題。
當正面和負面的網上消費者的評論同時存在時，正面和負面消費者的評論數量上的 差異可能會影響產品的態度。例如，一個網頁上有八條正面評論和兩個負面的評論，
量一種情況是有 80 條正面評論和 20 條負面評論。儘管相同的比例，但效果有可能是 不一樣的。因此日後可以設立不同的數量做研究。
此外，企業可以支付獎勵給個人來提高正面評論的數量，操縱消費者的評論來影響 消費者的態度和購買決策。基於這個原因，消費者會懷疑正面消費者的評論。未來可 以研究在網路欺詐消費者評論的環境下，關注的網上的評論對消費者的影響。
42
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附錄一:虛擬購物網頁
46
第二組
47
第三組
48
第四組
49
附錄二:SAS CODE PART1:針對假說 H1 H2 H3 H4 H5
PROC IMPORT OUT= WORK.paper
DATAFILE= "C:\SAS_PRG1\DATA\Paper\Test.xls"
PROC UNIVARIATE DATA=PAPER ; VAR Point;
RUN;
/*其中1代表HIGH 2代表LOW*/
DATA PAPER_NEW;
SET PAPER;
RUN;
PROC FORMAT;
VALUE SEXF 1='MALE' 2='FEMALE';
VALUE AGEF 1='14' 2='15-24 3='25-34' 4='35-44' 5='45-54' 6='55-64' 7='65';
VALUE HOURSF 1='0~2' 2='2~4' 3='4~6' 4='6';
RUN;
/*各組態度平均*/
PROC MEANS DATA=PAPER_NEW;
Class QUALITY LEVEL;
VAR ATTITUDE;
RUN;
/*敘述統計量表*/
PROC FREQ DATA=PAPER_NEW;
FORMAT SEX SEXF. AGE AGEF. HOURS HOURSF.;
TABLE LEVEL SEX AGE HOURS;
TITLE "FREQ TABLE";
RUN;
/*針對資訊素養統計量*/
PROC MEANS DATA=PAPER_NEW;
CLASS LEVEL;
VAR BASIC MODERN ADVANCE;
TITLE "SKILL MEANS";
RUN;
50 /*操作型變數檢測(針對三項能力)*/
PROC TTEST DATA=PAPER_NEW;
CLASS LEVEL;
VAR BASIC MODERN ADVANCE;
TITLE "SKILL TEST";
RUN;
/*ANCOVA檢定*/
PROC GLM DATA=PAPER_NEW;
CLASS PERCENT QUALITY LEVEL;
MODEL ATTITUDE=PERCENT|QUALITY|LEVEL KNOLEDGE/SS3;
OUTPUT OUT=OUTDS1 R=RESID1;
TITLE "H1 H2 H5 ANCOVA";
RUN;
/*ANCOVA模型檢定*/
PROC UNIVARIATE DATA=OUTDS1 NORMAL;
VAR RESID1;
TITLE "RESID NORMAL";
RUN;
/*比較數量多寡*/
PROC TTEST DATA=PAPER_NEW;
CLASS PERCENT;
VAR ATTITUDE;
TITLE "PERCENT ATTITUDE";
RUN;
/*比較品質高低*/
PROC TTEST DATA=PAPER_NEW;
CLASS QUALITY;
VAR ATTITUDE;
TITLE "QUALITY ATTITUDE";
RUN;
PROC GLM DATA=PAPER_NEW;
CLASS PERCENT QUALITY LEVEL;
MODEL CREDIBLE=PERCENT|LEVEL KNOLEDGE/SS3;
OUTPUT OUT=OUTDS2 R=RESID2;
TITLE "H2 ANCOVA";
RUN;
PROC UNIVARIATE DATA=OUTDS2 NORMAL;
VAR RESID2;
TITLE "RESID2 NORMAL";
RUN;
PROC GLM DATA=PAPER_NEW;
CLASS PERCENT QUALITY LEVEL;
MODEL PRACTICALLY=QUALITY|LEVEL KNOLEDGE/SS3;
OUTPUT OUT=OUTDS3 R=RESID3;
TITLE "H3 ANCOVA";
RUN;
51
PROC UNIVARIATE DATA=OUTDS3 NORMAL;
VAR RESID3;
TITLE "RESID3 NORMAL";
RUN;
PROC TTEST DATA=PAPER_NEW;
CLASS QUALITY;
VAR PRACTICALLY;
TITLE "QUALITY PRACTICALLY";
RUN;
PART2:針對假說 H6 H7
PROC IMPORT OUT= WORK.paper1
DATAFILE= "C:\SAS_PRG1\DATA\Paper\Test1.xls"
PROC GLM DATA=PAPER1;
WHERE LEVEL=1;
CLASS PERCENT QUALITY;
MODEL ATTITUDE=PERCENT|QUALITY KNOLEDGE/SS3;
OUTPUT OUT=OUTDS4 R=RESID4;
TITLE "H6 ANCOCA";
RUN;
PROC UNIVARIATE DATA=OUTDS4 NORMAL;
VAR RESID4;
TITLE "RESID4 NORMAL";
RUN;
PROC GLM DATA=PAPER1;
WHERE LEVEL=2;
CLASS PERCENT QUALITY;
MODEL ATTITUDE=PERCENT|QUALITY KNOLEDGE/SS3;
OUTPUT OUT=OUTDS5 R=RESID5;
TITLE "H7 ANCOVA";
RUN;
PROC UNIVARIATE DATA=OUTDS5 NORMAL;
VAR RESID5;
TITLE "RESID5 NORMAL";
RUN;
52 PROC MEANS DATA=PAPER1;
CLASS LEVEL PERCENT QUALITY;
VAR ATTITUDE;
RUN;
PART3:針對信效度
PROC IMPORT OUT= WORK.SEM
DATAFILE= "C:\Users\Loverainbow\Desktop\Test2.xls"
DATA SEM_NEW;
SET SEM;
RUN;
/*CFA*/
PROC CALIS DATA=SEM_NEW cov residual modification MAXFUNC=5000 MAXITER=5000;
LINEQS X23=L23 F2+E23, X24=L24 F2+E24, X25=L25 F2+E25, X26=L26 F2+E26, X27=L27 F2+E27, X31=L31 F3+E31, X32=L32 F3+E32, X33=L33 F3+E33, X34=L34 F3+E34, X41=L41 F4+E41, X42=L42 F4+E42, X43=L43 F4+E43, X51=L51 F5+E51,
53 X52=L52 F5+E52,
X53=L53 F5+E53, X61=L61 F6+E61, X62=L62 F6+E62, X63=L63 F6+E63;
STD
F1-F6=1*6,
E11-E13 E21-E27 E31-E34 E41-E43 E51-E53 E61-E63=VARE11-VARE13 VARE21-VARE27 VARE31-VARE34 VARE41-VARE43 VARE51-VARE53 VARE61-VARE63;
COV
F1 F2=cF1F2, F1 F3=cF1F3, F1 F4=cF1F4, F1 F5=cF1F5, F1 F6=cF1F6, F2 F3=cF2F3, F2 F4=cF2F4, F2 F5=cF2F5, F2 F6=cF2F6,
F3 F4=cF3F4, F3 F5=cF3F5, F3 F6=cF3F6, F4 F5=cF4F5, F4 F6=cF4F6,
F5 F6=cF5F6;
VAR
X11-X13 X21-X27 X31-X34 X41-X43 X51-X53 X61-X63;
TITLE 'CFA';
RUN;