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Reconsidering Pay Dispersion’s Effect on
the Performance of Interdependent Work:
Reconciling Sorting and Pay Inequality
Charlie O. Trevor
University of Wisconsin-Madison
ctrevor@https://www.doczj.com/doc/2817506145.html,
Greg Reilly
University of Connecticut
greilly@https://www.doczj.com/doc/2817506145.html,
Barry Gerhart
University of Wisconsin-Madison
bgerhart@https://www.doczj.com/doc/2817506145.html,
ABSTRACT
Pay dispersion in interdependent work settings is virtually universally argued to be detrimental to performance. We contend, however,that these arguments often confound inequality with inequity, thereby overestimating inequity concerns. Consequently, we adopt a sorting (attraction and retention) perspective to differentiate between pay dispersion that is used to secure valued employee inputs and pay dispersion that is not.We find that the former is positively related to interdependent team performance, the latter has no effect or is detrimental, and the approach itself helps to reconcile the pay dispersion literature’s disparate results.Curvilinearity tests reveal potential constraints on the sorting argument.
Because interdependent work requiring substantial employee interaction to complete core tasks is commonplace in today’s organizations, it is critical to understand how to most effectively manage employees in this type of setting. Pay dispersion, a topic of great interest in the recent management literature, is virtually always presumed to be counterproductive when work is interdependent. Numerous authors from economics (e.g., Akerlof & Yellen, 1988; Hicks, 1963; Levine, 1991) and management (e.g.,Bloom, 1999; Ferraro, Pfeffer, & Sutton, 2005; Harrison & Klein, 2007; Shaw,Gupta, & Delery, 2002) argue that pay dispersion in an interdependent work context causes inequity perceptions corrosive to employee attitudes, commitment, and cooperation, thereby hampering collective(i.e., team, unit, organizational) performance.In contrast, we argue here that the commonly held conceptual arguments against pay dispersion in interdependent work settings conflict with both relevant theory and the extant empirical research. It is, for example, difficult to reconcile the inequity-based denunciation of pay dispersion in interdependent work settings with the substantial pay-for-performance literature that advocates paying unequal contributors unequally(as failure to do so is itself an inequitable practice [Brown, Sturman, & Simmering, 2003; Heneman & Werner, 2005]).
Such inconsistency, however,is critical to redeveloping theory because it provides the opportunity to“challenge the value of a theory and to explore its weaknesses and problems”(Alvesson & Karreman, 2007; p. 1265), which is our goal here. We believe the inconsistencies in this case to be consequences of conceptual and empirical approaches to employee inputs(e.g., equating pay inequality with pay inequity)that in fact are incompatible with key tenets of not only equity theory, but of sorting principles(i.e., the attraction and retention of quality employees) as well. This conceptual and empirical confluence risks leading scholars and professionals to adopt conceptual approaches and pay practices that are of questionable validity.
Thus, we aim to challenge the conventional wisdom with a theory-grounded framework that both contradicts the inequity-based critiques of pay dispersion in interdependent work and largely resolves the discrepancies in the relevant empirical research.
While authors studying the pay dispersion-performance relationship, at all levels of work interdependence, tend to pit the incentive potential of pay dispersion against its potential inequity-driven disruptiveness (e.g., Bloom, 1999; Kepes et al., 2009; Pfeffer & Langton, 1993; Shaw et al., 2002), we move beyond these existing perspectives by theorizing that sorting is a critical mechanism through which pay dispersion, even in highly interdependent work settings, can facilitate group performance. Additionally, we differentiate between pay dispersion that is explained by productivity-relevant employee inputs and pay dispersion that is not to illustrate why both positive and negative dispersion effects on performance emerge in the literature.We then, in a highly interdependent work context, test whether pay dispersion and team performance are positively related when (and only when) pay dispersion is explained by the sorting of employee inputs. Finally, given recent nonlinear approaches to the impacts of pay dispersion (Brown et al., 2003) and human capital (Ployhart, Weekley, & Ramsey,2009), we explore possible curvilinear dispersion effects that would reveal constraints on the sorting rationale.
CONCEPTUAL DEVELOPMENT
Key Definitions and Model Components
We study pay dispersion in a team-based context in which the tasks required for team success are highly interdependent, defining interdependence as the degree to which completion of the organization’s tasks require employees to interact (Cummings. 1978; Thompson, 1967). We investigate lateral pay dispersion within the dominant job in an industry. Lateral (or horizontal) pay dispersion involves pay differences across employees within the same job or
within a single organizational level (Bloom,1999; Pfeffer, 1994). In contrast to vertical pay dispersion(i.e., pay differences between employees in jobs at different hierarchical levels), lateral pay dispersion is often the focus when there are many incumbents in a single core job, as is frequently the case in more professionalized occupations (e.g., sports professionals, physicians, nurses, lawyers, accountants, teachers, academicians, etc.).
Central to our thesis is an inputs-based differentiation of pay dispersion types. Dispersion in explained pay(DEP) is the amount of variation in the employee pay that is tied to sorting, the acquisition and retention of productivity-relevant employee inputs(see Figure 1).Specifically, because certain employee inputs (typically job performance) are associated with organizational productivity, they are productivity-relevant(and thus strategically-relevant)explanations for pay dispersion under both independent and interdependent work. Pay dispersion associated with the securing of these productivity-relevant employee inputs(via attraction and retention)is, by definition, DEP, as illustrated in Figure 1.In contrast, dispersion in unexplained pay(DUP) is the amount of variation in the employee pay that is unexplained by these productivity-relevant inputs. Thus, DUP is dispersion in the pay that is attributable to factors unrelated to employee productivity(e.g., politics, discrimination, favoritism, random decisions, etc.).
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Insert Figure 1 and Table 1about here
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Researchers to date have typically studied overall pay dispersion, with each study’s statistical modeling specification determining (at times inadvertently, in our view) whether DEP or DUP was primarily at work. In Table 1 we summarize the empirical pay dispersion—performance research, along with our informal assessment of whether the pay dispersion
modeled more closely represented DEP or DUP. Such classification should allow for a better understanding of the disparate research findings(we return to Table 1in more detail later).
Pay Dispersion and Sorting
The compensation literature(Gerhart & Rynes, 2003; Lazear, 2000) classifies pay effects on employee behaviors as associated with either incentives or sorting. While we believe that incentive pay can, under certain circumstances, have positive implications for team performance, our emphasis here is on sorting, which, as an explanation for interdependent performance, has been virtually ignored in the pay dispersion literature.The sorting premise stipulates that pay linked to inputs can yield human capital advantages by serving to attract and retain higher ability, better performing employees (Gerhart & Rynes, 2003; Lazear, 2000). In part because pay dispersion, pay level, and pay-for-performance practices are jointly tied to the exact same aggregated individual pay level decisions, there is indirect evidence that greater dispersion in the pay linked to productivity-relevant employee inputs (i.e., DEP) should facilitate such sorting. Larger pay differentials (and thus more DEP) are frequently characterized by high pay levels needed to secure the most talented employees, with research indicating that high pay level increases the ability to attract and retain the most talented workers (Krueger, 1988; Shaw, Delery, Jenkins, & Gupta, 1998). Similarly, pay-for-performance, as distinct from pay level, also appears to be associated with both larger inputs-based pay differentials (and thus more DEP) and sorting advantages, in that it positively affects the attraction of high ability,high performing employees (Cadsby et al., 2007; Lazear, 2000) and the retention of high performers (e.g., Salamin & Hom,2005; Trevor, Gerhart, & Boudreau, 1997).
Although pay level and DEP are strongly related and thus have some similar sorting effects, DEP also has unique sorting advantages. Individuals, including those with high
performance and talent inputs, depend on social comparisons to evaluate their pay (e.g., Adams, 1963; Barnard, 1938; Pfeffer & Langton, 1993) and react to the degree of perceived advantage in these comparisons. DEP results in those high in the pay distribution (via performance or talent inputs) having high relative pay, and evidence shows that pay dispersion strengthens perceptions of favorable pay for these employees (Trevor & Wazeter, 2006). As a result, individuals located near the top of a highly dispersed pay distribution should feel relatively advantaged and, consequently, be less likely to quit (Pfeffer & Davis-Blake, 1992). Additionally, on the attraction side of sorting, because individuals enjoy the status associated with higher rank in a pay hierarchy, they have an incentive to choose the hierarchy in which they will enjoy the most relative pay and status advantages (Frank, 1985), which will be a distribution with high DEP. Finally, we note that a low-DEP alternative, paying everyone at the same high level, loses the social comparison-based sorting advantages, drives up labor costs, and assumes that all individuals and their roles are equally critical to success and thus require top-of-the-market pay.
Little empirical work, however,exists on whether pay dispersion in general, or DEP in particular, has sorting consequences. Using a sample of high-level administrative personnel from multiple academic institutions, a context that appears to be very low in work interdependence, Pfeffer and Davis-Blake (1992) found support for pay dispersion’s sorting potential; specifically, for those high in the pay distributions, turnover diminished as pay dispersion (and presumably DEP) increased. Similarly, Shaw and Gupta(2007) found that, when performance-based increases were emphasized and the pay system was well-communicated (a likely context for DEP),greater pay dispersion predicted a reduction in the number of high performing truck drivers that quit.However, because this trucking sample(like the Pfeffer and Davis-Blake, 1992, sample)is not an interdependent work setting (Shaw et al., 2002, p. 495), the relevance of pay
dispersion to the retention of interdependent workers remains untested.Further, we found no research directly addressing pay dispersion effects on employee attraction. To formally examine the sorting premise that is the foundation for our unconventional team-performance predictions, we propose that teams with high DEP (i.e., teams that more closely tie pay to productivity-relevant inputs) will fare better in the attraction and retention of high inputs employees (i.e., those who previously have performed at high levels).
H1: Relative to low DEP teams, high DEP teams will attract more high inputs players
and retain more high inputs players.
DEP’s Sorting Effects (and Lack of Inequity Effects) on Team Performance
Sorting advantages that accrue to DEP should, all else equal, contribute to enhanced team performance.But while there has been some recent progress in understanding the favorable incentives implications of pay dispersion when paying for inputs in a highly independent work context(see Shaw et al.[2002]and Kepes et al. [2009]), scholars agree that, in interdependent settings, pay dispersion is particularly detrimental to aggregate performance (e.g., Akerlof& Yellen, 1988; Bloom,1999; Ferraro, Pfeffer, & Sutton, 2005; Franck & Nuesch, forthcoming; Harrison & Klein, 2007; Hicks, 1963; Levine, 1991; Kepes et al., 2009; Pfeffer & Langton, 1993), even if the dispersion is tied to inputs. Authors linking pay dispersion with unfairness at any level of work interdependence typically cite equity theory(Adams, 1963) principles to argue that large pay differentials yield inequity perceptions, psychological distress, reduced cooperation, disharmony, lower commitment, and increased turnover (e.g., Akerlof & Yellen, 1988; Bloom, 1999; Ferraro et al., 2005; Lazear, 1989; Levine, 1991; Pfeffer & Langton, 1993).
Pay dispersion, however, tells us only that pay allocation is unequal, not that it is inequitable. Leventhal (1976) and Steers and Porter (1983) describe pay equality as equal pay for
all employees (i.e., low pay dispersion, regardless of pay equity)and pay equity as pay proportionate to employee inputs (i.e., potentially any level of pay dispersion, as long as pay is tied to productivity-relevant inputs). This equality versus equity difference, though often overlooked, is crucial: pay dispersion implies pay inequality, DEP implies pay equity, and only DUP implies pay inequity (see Figure 1). The equity and fairness literature clearly stipulates that it is pay inequity,rather than pay inequality,that prompts negative employee reactions (e.g., Ambrose & Kulik, 1999; Deutsch, 1985; Heneman & Judge, 2000; Leventhal, 1976). Therefore, pay dispersion that is explained by inputs regarded as productivity-relevant should be perceived as equitable and should not yield counterproductive responses. Hence, the idea that DEP (or even overall pay dispersion if primarily DEP) should yield negative reaction is not, contrary to what has often been contended, consistent with equity and fairness theories.
As long as individual contributions can be identified, nothing in the above argument is particular to independent work. In fact, in interdependent contexts where individual contributions are identifiable, inequity perceptions are more likely without pay dispersion, as pay equality, assuming variation in inputs, produces pay inequity.Indeed, researchers that focus on social loafing, which is the undesired tendency for people to reduce effort and productivity when in groups, maintain that money tied to individual inputs (Sheppard, 1993; p. 70) “can serve as powerful incentives for behavior, countering the reduction in effort typically exhibited by participants who are combining their efforts.” In this well-developed literature,it is the task interdependence itself that leads to motivation loss, while pay tied to individuals’ productivity-relevant inputs, and the subsequent DEP,is a tactic to combat it. Thus, when work is interdependent and pay is tied to individual inputs, we find little conceptual support for the predictions that pay dispersion will lead to perceived inequity and its behavioral fallout.
In terms of empirical support, perhaps because the conventional wisdom has so often been presumed to be true, only four Table 1 studies (Bloom, 1999; Eriksson, 1999; Main et al., 1993; Shaw et al., 2002)explicitly reported testing whether work interdependence would result in negative pay dispersion effects on performance or would mitigate any otherwise favorable pay dispersion effects. Of these, only Bloom(1999)reported clear support(later we argue that this study involves neither interdependent work nor DEP). Negative pay dispersion effects are reported in a number of studies in which work interdependence is not addressed, however, and it is likely that these have fueled the popular conception of pay dispersion as corrosive to teamwork.As we focus on in the next section, though, most of the empirical literature cited in support of detrimental pay dispersion effects, regardless of work interdependence, appears to be modeling DUP rather than DEP. DUP, by definition, has none of the sorting advantages of DEP, and is more susceptible to the inequity-based problems so often cited(see Figure 1).
In sum, under our conceptual model, and as predicted in H1,DEP should yield sorting advantages when work is interdependent (or independent for that matter), as larger pay differentials based on productivity-relevant inputs facilitate top talent attraction and retention. As is often stipulated (e.g., Barnard, 1938; Becker & Gerhart, 1996; Pfeffer, 1994), and as has been empirically demonstrated (e.g., Ployhart et al., 2009), enhanced human capital results in greater aggregate-level productivity. Thus, to the degree that DEP sorts higher aggregate human capital to the team, the effect on performance should be positive. Furthermore, DEP, by definition, entails pay inequality but not the pay inequity that fairness theories identify as problematic. Finally, the social loafing literature (e.g., Sheppard, 1993) stipulates that there is incentive value from pay tied to inputs in interdependent work.Consequently, we do not see DEP in the presence of work interdependence as likely to generate the considerable inequity perceptions and effort
reduction necessary to overwhelm DEP’s sorting benefits.To address this, we provide the first empirical test of the entire pay dispersionàinputsàgroup productivity causal chain.
We first isolate DEP and DUP measures,then test for a DEP effect and for differences between DEP and DUP effects. Second(see Figure 1), we test whether the pay dispersion that is used to secure (is mediated by) employee inputs (i.e., the sorting effect) will have a positive effect on performance. This indirect effect, by definition, is a DEP effect.We then compare the indirect (DEP) effect of the pay dispersion used to secure inputs with the direct (DUP) effect of the pay dispersion that is independent of inputs; thus,we differentiate the pay dispersion that should facilitate team performance (DEP) from the pay dispersion less apt to do so (DUP).
H2a: Pay dispersion explained by productivity-relevant employee inputs (DEP) will be positively related to team performance; this positive effect will be more favorable than
the effect of pay dispersion that is net of inputs (i.e., DUP).
H2b: Productivity-relevant employee inputs will mediate overall pay dispersion’s
relationship with team performance, resulting in a positive indirect, sorting effect (i.e., a positive DEP effect); this sorting (DEP) effect will be more favorably related to team
performance than will be the direct (DUP) effect.
Modeling, the Loss of DEP, and the Emergence of DUP
Given our arguments that DEP should positively affect organizational performance in interdependent work settings, why do studies often report negative overall pay dispersion effects?One reason may be that organizations actually do at times fail to adequately tie pay to productivity-relevant inputs such as performance(e.g., Kahn & Sherer, 1990; Heneman & Werner, 2005; Schwab & Olson, 1990), which negates sorting benefits and makes more likely the often-cited, inequity-driven problems.We argue that a second reason for negative pay
dispersion effects, at all levels of work interdependence, is a methodological artifact, in that DUP, which has none of DEP’s sorting benefits,is sometimes inadvertently modeled in this research. Our concern is that, unless explicitly studying DUP(e.g., as in Cowherd & Levine’s 1992 study), researchers may mistakenly attribute DUP effects to total pay dispersion or, worse yet, to DEP, thereby perpetuating the belief that pay dispersion is inherently detrimental.
Specifically, we contend that, via decisions about what covariates to include in regression models, authors have at times largely partialled out the DEP from total pay dispersion, thus increasingly leaving only DUP(i.e., H2b’s and Figure 1’s direct effect of dispersion that is independent of inputs and not expected to have positive effects). For example, pay level strategy (mean pay) appears as a control variable in several analyses of pay dispersion effects (see Table 1). One argument for including it is to account for a high wage effect that would manifest in talent advantages to organizations that paid more (e.g., Bloom, 1999). This rationale, however, is precisely why our emphasis on pay tied to productivity-relevant inputs necessitates a different modeling approach. Controlling for mean pay level “parcels out the positive effects of pay dispersion: attraction and retention of star players who are paid a great deal, thus resulting in better team performance, higher team pay, and greater dispersion”(Gerhart &Rynes, 2003, p. 182).In a second argument, Harrison and Klein(2007) contend that researchers should control for the within-group mean because it may be confounded with the disparity measure. In a pay-setting context, however, such confounding is exactly why we examine pay dispersion effects both with and without the mean controlled. Because paying for talent yields both high mean pay and high pay dispersion (Gerhart & Rynes, 2003), we focus on distinguishing the dispersion that covaries with high talent and mean pay (i.e., DEP) from the dispersion that does not (i.e., DUP). Isolating the degree of this covariation, or “confound,”is central to our contribution.
Similarly, partialling out a pay-for-performance strategy measure risks parsing out DEP and increasingly leaving DUP as the dispersion modeled. Despite their insightful discussion regarding fair bases for pay dispersion, Pfeffer and Langton (1993) partialled out the within-department correlation between employee pay and productivity(which correlated with pay dispersion at .31) when predicting faculty productivity.Thus, they actually modeled only the pay dispersion that was unrelated to the pay tied to job performance inputs, likely largely capturing DUP effects. Moreover, the pay-productivity correlation was positively related to productivity in the Pfeffer and Langton data (Gerhart & Rynes, 2003), suggesting that, in contrast to the detrimental pay dispersion effect reported by the study’s authors, conditions were present that would make a positive DEP effect likely.
As with partialling out pay strategies that yield productivity-relevant inputs, controlling for productivity-relevant inputs themselves also can yield the modeling of DUP and pay dispersion effects that do not reflect dispersion’s sorting benefits.Bloom(1999), for example, in analyses leading to the reporting that pay dispersion hindered performance in interdependent work, partialled out a measure of baseball team talent. Similar partialling of productivity-relevant inputs occurs in several other studies reporting negative pay dispersion effects (e.g., Frank & Nuesch, forthcoming; Jewell & Molina, 2004; Leonard, 1990; Siegel & Hambrick, 2005). Such an approach, however, serves to parcel out the positive sorting effects of pay dispersion (Gerhart & Rynes, 2003), likely leaving DUP as the pay dispersion modeled in the regression analysis.
As an informal exploration of our belief that these modeling issues have led to confusion in the field, we applied our DEP/DUP logic to the extant work in Table 1. Thus, the table includes our judgment of whether DEP or DUP was the primary type of pay dispersion modeled in each study(see the table note for our decision rules). These judgments found that, while three
studies yielded results counter to our framework and three were ambiguous, 17 studies supported our positions: in general, DEP yields positive effects, DUP yields zero or negative effects, and partialling out pay strategies and the productivity-relevant employee inputs they produce leaves less DEP, more DUP, and lower likelihood of observing the sorting benefits that sound pay policy can provide.Thus, we contend that, in both independent and interdependent work, the pay dispersion effects reported often strongly depend on how pay dispersion is modeled.
H3: In an interdependent work setting, controlling for pay level strategy, pay-for-
performance strategy,and productivity-relevant employee inputs will partial out
positive effects of pay dispersion on team performance, resulting in a negligible or
negative pay dispersion effect.
Pay Dispersion and Curvilinearity
Although we have thus far challenged the inequity-based critique of pay dispersion and promoted a more favorable sorting-based characterization of DEP, we also acknowledge that a more nuanced approach to DEP may be necessary. Brown et al. (2003), for example, while recognizing the value of linking pay to inputs, still theorized on inequity grounds that pay that
is too widely dispersed may be detrimental. At some point, even pay differences clearly explained by inputs might be seen as too large,and thus inequitable.Indeed, organizations attempt to ensure that performance-based pay differences between individuals are not disproportionate to actual performance differences (Milkovich & Newman, 2008).With such disproportion, pay dispersion may still be “explained,” but the explanation may be deemed inadequate, and the pay inequitable, given the extreme differentials.Thus, individual incentive effects in groups, such as those identified in the social loafing literature (e.g., Sheppard, 1993), could be tempered by inequity concerns when the subsequent pay differentials (i.e., DEP) are
very large. Moreover, any such inequity perceptions driven by extreme DEP may constrain the sorting advantages DEP would normally provide, as under-reward inequity perceptions can lead to voluntary turnover (Gerhart & Rynes, 2003; Heneman & Judge, 2000).
There is an additional basis for expecting diminishing sorting returns to DEP. There are often inherent limitations in an organization’s development of certain capabilities,such as the capacity to utilize resources(Helfat & Peteraf, 2003). That is, as talent resources (inputs) grow beyond a certain point, the organization may become less effective at managing them, thus constraining the leveraging of DEP-driven talent into performance. For instance, work teams may perceive some level of a capability such as managing talent as satisfactory and refrain from developing the capability further (Winter, 2000). Based on this capability argument, Ployhart et al. (2009) hypothesized and found that the positive effect of sales force human capital on store performance diminished at higher human capital levels.Similarly, Barry and Stewart (1997) found a curvilinear effect of the personality dimension extraversion on group performance, perhaps indicating “too much of a good thing” (Gerhart & Rynes, 2003). Hence, despite DEP’s sorting advantages, the enhanced inputs that greater DEP yields may not be accompanied by proportionate increases in managing those inputs and, subsequently, in team performance. This potential, coupled with the inequity-based argument for diminishing returns to DEP, suggests the following nonlinear relationship.
H4: The positive effect of DEP on team performance will be attenuated at high
levels of DEP.
METHODS
To test our hypotheses, we study National Hockey League (NHL) teams, a population in which data on pay and readily observable performance are available at both individual and
organizational levels, and in which there is considerable player movement across teams. Also, team performance in hockey depends on strong work interdependencies (Beaucamp & Bray, 2001; Foster & Washington, 2009; Frey et al., 1986; Gerhart & Rynes, 2003), which is regularly cited as the context in which pay dispersion will be particularly disruptive (e.g., Akerlof & Yellen, 1988; Bloom,1999; Levine, 1991; Pfeffer & Langton, 1993; Pfeffer, 1994).
Data
The individual player and team performance data used for this study are from the official records of the NHL. We acquired these records from the websites https://www.doczj.com/doc/2817506145.html, and https://www.doczj.com/doc/2817506145.html,,as well as annual editions of the NHL’s Official Guide & Record Book (e.g., National Hockey League, 2002). The dataset used for the team-level analyses consists of pay and performance data for each team in the NHL during each of the seasons ending in 1998 through 2004. Thus, the dataset consisted of 201 total team-years, as the league housed 26 teams in 1998 and expanded to 27 teams in 1999, 28 teams in 2000, and 30 teams in 2001-2004.The usable sample for our team-level analyses dropped to 175, as we used year t-1measures of productivity-relevant employee inputs when predicting year t team performance.
Our team-year measures were built from an initial dataset of 4,465player-year observations, which included annual performance and salary statistics for each individual non-goalie player that played in the league during our study period. We focused on non-goalies because performance criteria (and thus pay-for-performance strategies) are distinctly different for goalies; moreover, the small number of goalies per team (two or three) precludes reliable measures of within-team pay dispersion for that group of players. Also, to enhance reliability in our productivity-relevant measures, we limited inclusion in the database to individual players who appeared in at least 20 of their team’s 82 games in any given season.
Dependent Variable
Team performance. Our first on-ice measure, points, is calculated by the NHL by summing two points for each regular season win, one point for each tie, and one point for each overtime loss(this component was added by the NHL two years into our study window). Points determine position in the standings during the regular season of play, which teams make the playoffs, and how those teams will be seeded in the playoffs. Because the ultimate on-ice goal of NHL teams is to win the Stanley Cup championship, however, our second on-ice measure, round, is each team’s final position in the playoff tournament bracket. Each year 16 teams make the playoffs, with teams advancing to subsequent rounds only by winning a best-of-seven series of games. Round takes on the following values for each year: 0 (all non-playoff teams); 1 (8 teams that lost in the first round); 2 (4 teams that lost in the second round); 3 (2 teams that lost in the third round); 4 (1 team that lost in the fourth round); and 5 (1 championship team). Because playoff injuries, opponent match-ups, and reduced variance in the measure suggest that round will be less reliably predicted than points, we expected that the support for our hypotheses might be weaker when predicting round. We restrict our analysis to the prediction of on-ice performance because the linkage between pay strategies and team off-ice performance (e.g., profitability) is significantly more distal and tenuous.
Independent Variables
Because empirically isolating DEP and DUP is challenging, we take three approaches (partialling, mediation, and predicted values/residuals) and assess whether our findings are robust to technique choice. We summarize our approach to the key predictors in Table 2.
Inputs. Job performance is the classic employee input from a rewards perspective (Steers & Porter, 1983), and is the basis for our sorting arguments for pay dispersion effects on
team performance. Based on a measure of individual player value officially approved by the National Hockey League Players’ Association (NHLPA) for use in fantasy hockey league play, our inputs measure represents the performance aspect of the productivity-relevant employee inputs depicted in Figure 1. The sanctioned measure is the sum of seven on-ice performance components: goals, assists, plus/minus (the differential between goals scored and allowed when the individual is on the ice and the team scoring is not on a power play), power play and shorthanded goals and assists (we use goals here, as power play and shorthanded assist data were unavailable for all years; our amended formula produced scores that correlated with the official measure’s scores at .994 in 2002-2004), penalty minutes, shots on goal, and defensive goals and assists (ESPN, 2007). Each component was standardized (within-year) prior to the addition of the components.To account for injury and other sources of unreliability among inputs, we used a two-year average (from years t-1and t-2) when the data were available.We used the within-team-year mean of individual inputs to create team-year inputs(i.e., the team-year level of productivity-relevant employee inputs). Inputs correlated with raw salary at .69at the individual level and .79 at the team-year level. Year t-1inputs are used to predict year t performance.
Pay variance. We use pay variance within each team-year observation to operationalize pay dispersion. Unlike some previously used dispersion measures, variance does not explicitly factor out the mean and is thus consistent with our emphasis on avoiding the partialling of mean pay effects (as noted in the Discussion section, alternative dispersion measures yielded results highly similar to those found with the variance operationalization).
Dispersion in explained pay(DEP). To estimate DEP and DUP, we used an individual-level, league-wide regression of logged (due to extreme positive skew) CPI-adjusted pay on individual-level performance inputs. The regression equation used was:
Y it= P it-1A+ X it B+ e it,(1)
where Y= a vector of CPI-adjusted and logged pay level observations for player i in year t, A and B= regression coefficient vectors,X= a matrix of dummy variables representing years, e= an error term reflecting the residual for player i in year t, and P= a matrix of values from the individual-level inputs measure and its square from year t-1.
The R for this equation was .72and the R2was .52, indicating that just over half of the individual pay level in the population was a function of the observable performance data captured in the individual-level inputs measure. Each player’s predicted value of pay level (i.e., ?it) from this league-wide regression represents their expected pay, given how the entire market rewards the observable inputs in the equation. Thus, within-team variation in these predicted pay values is variation in pay that can be explained by observable productivity-relevant data. Consequently, our measure of DEP_predicted is simply the variance of the predicted values of individual pay level for all players on the team-year observation(i.e., ?2?it).
Dispersion in unexplained pay(DUP). To measure DUP, we again make use of the league-wide individual-level regression described in equation (1). Each residual from the analysis is the individual pay that is independent of the productivity-relevant player observables. Thus, the variance of these residuals (within team-year) represents pay dispersion that is unexplained by these data. Because this variance of player residuals is dispersion in unexplained pay, we refer to this measure as DUP_residual.
DEP and DUP from model specification.Pay dispersion used to secure productivity-relevant employee inputs is, by definition, DEP. Consequently, in mediation models where we test the indirect (sorting) effect of pay variance through employee inputs, this indirect effect is a DEP effect, while the remaining direct effect is a DUP effect. Consistent with our critique of the
modeling of pay dispersion in earlier research, we also assess DUP via our independent variable combinations in our analyses. Controlling for pay strategies and productivity-relevant employee inputs leaves DUP as the unpartialled component of pay variance(see Table 2and Figure 1).
Pay-for-performance strategy. We also correlated, within each team-year, individual inputs from year t-1and logged individual pay from year t to create the pay strategy variable, pay-for-performance(see Pfeffer and Langton, 1993, for a similar measure of pay-for-performance). Logged salary was used in these team-year correlations to account for the exponentially increasing pay returns as individual inputs increase.
Pay level strategy. Mean pay level is the average salary for individual players within each team-year and was used to measure pay level strategy. Before averaging, we adjusted the salaries for the Consumer Price Index (CPI), leaving all salaries in 1998 dollars.
Analysis
Dependent variable distributions. While our points dependent variable is relatively normally distributed, the rounds dependent variable is distributed as event count data. The Cameron and Triveldi (1986) regression-based test for overdispersion indicated that the conservative negative binomial regression model, rather than Poisson regression, was appropriate when predicting round.
Dependent observations.Because we needed to account for the fact that teams appeared in the data set an average of 5.83times, we used random and fixed effects models to reduce concern that any unmeasured organization-level variable could be driving both team pay and team performance. Random effects models produce matrix-weighted averages of the between-unit and within-unit effects, whereas fixed effects models, by definition, partial out stable between-unit differences, leaving the regression coefficients as estimates of purely within-unit
未来发展十大前景行业 【篇一：未来发展十大前景行业】 编辑:biyu 中国未来最有前景的行业是什么?我写这一段的主要目的 是给大家择业、投资等提供一些参考性的指导意见。按照前景的优 劣我想主要有以下几个行业： 1、排在第一位的是水务行业，包括水资源、污水处理、海水淡化技 术等。很多朋友或许会说，为什么会是水呢?因为水是一种谁也离不 开的、不可替代的、再生有较大成本的资源;而某种资源价值的高低 取决于需求和供给之间的关系，或者说资源的稀缺性。从需求方面 来看，随着中国经济的不断发展，对水的需求是刚性上涨的，而且 水的需求弹性是很小的，也就是说需求者愿意为了水资源出无穷高 的价格;另外13亿多人的中国，水需求无疑是相当巨大的。 来看供给，中国水资源的供给是相当紧缺的，看一些数据。中国水 资源总量达到2.8万亿立方米，但人均水资源占有量只有2200立方米，相当于世界人均水平的1/4;中国的水资源在时间和空间上分布 不均匀，夏秋多，冬春少;南多北少、东多西少;除了自然禀赋方面的 原因外，环境污染正严重影响中国的水资源供给;据官方统计，中国 半数以上的主要水道都受到污染，水既无法饮用，也无法用于灌溉，现有超过3亿人(接近中国1/4人口)缺乏干净的饮用水;据国家环保总局的监测，2005年全国七大水系的411个地表水监测断面中有27%的断面为劣Ⅴ类水质，全国约1/2的城市市区地下水污染严重;水体 污染实际上减少了可供人类使用的水资源数量，人为制造了水资源 紧缺。其实，缺水是全世界多数国家面临的问题，我猜想未来战争 的一个重要起因很可能是大国争夺水资源。 其实，现在已经有外资以高溢价收购或控股中国的水务项目。8月 18日，扬州自来水股权转让项目招标，转让49%的净资产价值1.8 亿元，中标公司有30年的指定区域内供水特许经营权;共有4家水 务公司进行投标，跨国公司中法水务以8.95亿元的投标价格独占鳌头，报价为资产价值的5倍左右。8月22日，天津自来水项目49% 股权转让，评估资产价格为7亿多元，其中，招标方的硬性条款规 定必须溢价30%以上在各竞标公司报价中，中法水务为11.9亿元，威立雅水务21.8亿元。对于外资高溢价收购各地水务项目，有控制 权的地方政府往往举双手赞成：一是可以增加地方政府的收益，二 是为当地引进了外资，三是现在没有提高水价，老百姓没受影响。
竞合谋变：银行与第三方支付的前世今生 时间:2013-03-04来源:第一财经日报 《第30次中国互联网络发展状况统计报告》显示,截至2012年6月,中国网民达5.38亿,通过手机上网的人数超过4.2亿,互联网正在深刻地改变人们的生活与消费方式。 随着网络购物的兴起和电子商务的渗透,以支付宝为代表的第三方支付公司应运而生。而央行发放支付牌照,“快捷支付”快速崛起,令第三方机构将业务从单纯的转接支付拓展到了转账汇款、代缴费、基金销售和小额融资等金融服务领域。不过同时也暴露出一定的风险隐患。 成长之路 第三方支付缘起网络购物,壮大于快捷支付,不断发力全场景支付和增值服务创新,在快速发展进程中伴生出风险和问题,双刃剑的效应凸显。 一、诞生与发展 C2C缘起于淘宝。2003年,在"非典"横行的日子里,国民大多选择在家里看电视而减少逛街,支付宝在电视上大做广告,赚足了眼球。淘宝集市从那时起,逐渐引领了中国网络购物的潮流和市场份额。“网购上淘宝”,成为年轻人的时尚。而在C2C的模式下,为了解决买卖双方的互信问题,支付宝模仿Paypal,承担了信用中介角色,第三方支付机构应运而生。 从诞生到今天,支付宝仍旧是中国市场份额最大的第三方机构,注册用户超过7亿。中国市场前三的支付机构占据了超过80%的市场份额,其中支付宝超过50%,财付通超过20%,银联在线系超过10%。2012年,仅支付宝处理的交易金额就超过1万亿。中国的第三方机构,有着区别于欧美的鲜明特点,那就是跑赢大市的支付机构均采用捆绑模式,通过捆绑集团的电商平台保证交易量:支付宝捆绑淘宝与天猫商城,财付通捆绑拍拍与QQ网购等。而Paypal和MoneyBookers等国外支付公司,一般从创立起就是独立的支付机构,属开放式合作模式;在香港、台湾等地区,在线支付业务也是由银行、卡组织、Paypal等独立支付机构承担。 自2004年起,随着电子商务的发展,中国的第三方支付机构像雨后春笋一样蓬勃发展。2011年5月,包括支付宝、财付通等第一批27家支付机构获发牌照;截至2013年1月,分六批累计发出223张支付机构牌照。牌照特许的经营范围包括:网络支付、预付费卡支付、移动支付、数字电视支付等。 监管机构边发牌照边监管也助力第三方支付的发展,央行2005年10月颁布《支付清算组织管理办法》;2009年4月央行对支付机构进行登记报备;2010年6月,央行发布《非金融机构支付服务管理办法》;2011年5月开始发放第三方支付机构牌照。市场上目前有223家机构在执照经营,而据不完全统计,包括还在申
前景最好的十大行业是哪些 导读：我根据大家的需要整理了一份关于《前景最好的十大行业是哪些》的内容，具体内容：，哪些行业是大家喜欢的，我为大家整理了，2017十大最有前途职业排行榜，希望大家喜欢!未来最吃香、最高薪、最有潜力、最有前景的行业都在这啦!!【通道入口】 最有"前途"的十大新兴职业 未来10年最有前景的十大高薪职业! 女生十大最吃香的职业最有前途的十大女生职业 2017年最有发展前景的十大专业 2017中国最热门的十大职业排行榜 未来几年，据说这9个行业前景最为乐观! 盘点未来最有前景及最具赚钱潜力的十大行业 未来中国最有前途的十大高薪职业 2017十大最有前途职业排行单 1.电子商务 2015年"双十一"双十一网购狂欢节让世界看到了中国的电商消费能力，仅当日一家公司的交易总额高达912亿人民币。据有关机构调查，电商未来的市场在十几万亿，因此造就的百万富翁也会多不胜数，可见电商领域潜力。而从事电商行业相关的职业在这种大的浪潮的推动下必将趁一把好东风。 2.新能源行业
第十八届五中全会制定能源"十三五"规划将加快建设安全、清洁、高效、低碳的现代能源体系。新能源的开发利用必定会成为国家能源发展的重点。推广新能源汽车，电动汽车正逐步走进人们的生活，今年的新能源汽车行业必将会引来很多外来资本投入，新能源的其他相关产业也必将成为能源行业的新宠。 3.教育和培训 在互联网时代组织的发展效率加速，因此对人才的需求也在加速，庞大的就业压力，越来越要求求职者的个人素质不断提高，据某人才招聘网站2015人才调查报告显示，2015年研究生学历求职是本科学历的1.倍，是大专学历的4倍之多。因此而必将产生更多的人员去参加培训，提高自己。2015年高级培训发展人才将是稀缺人才，同时更多是家长会重视孩子的教育问题，频繁出现的各种为孩子上学换户口，走后门可见一斑，所以可以预见未来幼儿教育市场发展潜力巨大。 4.O2O行业 2015年9月5日国务院办公厅印发《三网融合推广方案》全面推进"三网"融合，加快建设光纤网络,提高网络速度，加快物流快递发展，以互联网为载体，线上线下相结合层递消费这是一种新型的消费模式，发展起来会冲击传统的零售行业，据此产生的一系列衍生技术和行业也会兴起，比如移动支付，微商等。而关于这方面的人才，微商运营，移动支付技术，程序开发未来更是稀缺。 5.医疗保健行业 随着国人的生活水平和收入水平的同时提高，几十年前的生活陋习被摒
龙源期刊网 https://www.doczj.com/doc/2817506145.html, 银行与支付宝们争什么 作者：叶檀 来源：《大众理财顾问》2014年第05期 叶檀 著名财经评论人 阿里日子不好过，第三方支付再受重创。中国金融未来将走向银行主导、信用卡支付为主的模式。 4月10日，一份名为《中国银监会中国人民银行关于加强商业银行与第三方支付机构合 作业务管理的通知》（银监发10号，简称“10号文”）的文件正式下发，规范银行与第三方支付合作。这似乎破除了央行与银监会不和传闻，起码在互联网金融监管上，双方合拍。 10号文要求，首次建立业务关联时，必须通过第三方支付机构和银行的双重身份鉴别， 此前86号文可由第三方支付机构单独认证客户身份，5号文则要求商业银行识别。10号文再次强调银行在用电子渠道验证客户身份时，应采用双因素验证方式对客户身份进行鉴别。 阿里金融之所以能够做大，最大的功劳是对客户资料、交易信息的大数据分析，这才使得阿里小贷有可能以极低的成本对平台上几十万客户进行高周转的小额放贷，并且保证极低的坏账率。但现在银行规定必须进行身份识别，大笔支付受到严密控制，换句话说，第三方支付的客户信息、交易资料，银行可以尽情掌握，支付宝等与银行有博弈能力的第三方支付系统，也就失去了核心优势，而对正在跃跃欲试运用金融互联网进行大数据分析的银行而言，可谓得来全不费工夫。 银行加强了保密举措，10号文要求银行应构建安全的网络通道（如专线连接、VPN通道等），制定安全边界（如部署防火墙、DMZ隔离区等），防止第三方机构越界访问。 第三方支付规模日益斗大，尤其是第三方互联网支付增速极快，舌头舔到了银行的碗里。根据EnfoDesk易观智库《2011年中国第三方支付市场季度监测》数据报告显示，2011年中国第三方互联网支付市场交易全年交易额规模达到2.16万亿元，较2010年增长99%。2012年第三方互联网支付的交易规模达到3.8万亿元，同比2011年增长76%。到2013年，全年第三方支付机构各类支付业务的总体交易规模达到17.9万亿元，同比增长43.2%。其中线下POS收单和互联网收单分别占比59.8%和33.5%，移动支付增长明显，线上线下进一步融合。 支付宝是互联网第三方支付的龙头。2月8日，支付宝公司发布数据，截至2013年年底，支付宝实名用户已近3亿，其中支付宝快捷支付用户数是2.4亿，手机支付用户超过1亿。一旦进入二维码扫描支付时代，再绑定虚拟信用卡，智能手机支付将迎来爆发式增长。不仅支付宝，而是一批靠互联网起家的公司起舞，到那时候，恐怕几十万亿元的支付市场真没传统银行
技术与市场 第16卷第9期2009年 1.第三方支付 所谓第三方支付，就是一些和产品所在国家以及国外各大银行签约、并具备一定实力和信誉保障的第三方独立机构提供的交易支持平台。在通过第三方支付平台的交易中，买方选购商品后，使用第三方平台提供的账户进行货款支付，由第三方通知卖家货款到达、进行发货；买方检验物品后，就可以通知付款给卖家，第三方再将款项转至卖家账户。 2.第三方支付与银行关系的必要性 第三方平台是商家和银行之间建立连接，以实现从消费者到金融机构以及商家之间的货币支付、现金流转、资金清算、查询统计。其资金流动的本质是：资金在银行存取和转账。因此，银行机构成了第三方支付的一个重要因素，分析第三方支付与银行的关系对促进电子商务的发展起着举足轻重的作用。现以支付宝为例，进行简要分析，支付宝是中国电子支付样本。支付宝在C2C、B2C以及B2B领域的全面拓展，以及在国内第三方支付企业中的出色表现，都使其成为中国第三方电子支付的重要样本。2009年7月6日，支付宝（中国）网络技术有限公司宣布其用户数正式突破2亿大关，10个中国网民中就有6个使用。截至2009年3月31日，通过支付宝进行的电子商务日交易笔数峰值已达400万笔，日交易额峰值突破7亿元。而2007年仅支付宝所产生的支付流量就已经超过了2006年全国第三方支付企业网上支付的流量总和。因此，支付宝与银行的竞合极大程度上代表了第三方支付与银行关系的发展趋势。 3.支付宝与银行的关系几经转变 1)第三方支付与银行的互利合作。支付宝建立初期，就与诸多国内外银行建立了合作关系。从2006年3月开始，农业银行、工商银行、建设银行、浦发银行、民生银行等10多家银行在短短几个月内相继携手支付宝，共同开拓中国电子商务市场。 双方的合作，一方面解决了网上交易的安全信用问题，同时也为电子商务市场交易的需求提供了可能。第三方支付通过与银行的合作，获得了银行安全的支付网关接口，整合了不少中小商户资源，提升了规模和实力；网络银行分支机构也借第三方支付之力开拓了客户渠道发展了商家，并得到结算分成。 2)第三方支付与银行的合作进入瓶颈期。支付宝的免费模式让银行输不起。民生银行结束了与支付宝两年多的合作关系，其信用卡中心营销总监陈弘称其在支付宝上已经损失了800万元的资金成本；招商银行、光大信用卡中心也遭受了一定的损失，并对信用卡网上支付做出限制。 银行业90%的盈利依然来自于放贷的息差收入。银行遭受如此大的损失，原因在于：支付宝交易存在假消费、真套现行为。套现，指持卡用户以支付宝为交易中介，将信用卡信用额度转至借记卡，最终取得无息贷款。整个过程没有真实的货物交易，无需缴纳额外费用。而如果信用卡在ATM或银行柜台提现，则要支付不低的手续费和利息。 银行做出如此决策，最根本的问题是银行在其中无利可图。支付宝的信用卡交易是完全免费的，中国银行等各家银行的信用卡都是免费为支付宝提供网上支付服务，支付宝不付给银行任何费用。信用卡利润来源主要是年费、手续费和利息费。在国内信用卡营销大打价格战的情况下，年费已形同虚设，来自商家的手续费便成了信用卡最重要利润来源。用POS机刷卡消费，持卡人虽然无须支付额外费用，但商户却需向银行缴纳一定手续费。然而，在支付宝这个支付平台上，信用卡却收不到一分钱。 3)找到新的共赢点，继续携手同行。事实上，“共赢”仍然是银行与支付宝的主旋律。民生银行、中国银行等并没有完全切断与支付宝的合作，保留了其借记卡的支付宝交易功能。而中信银行，也在8月悄然恢复了信用卡支付宝交易功能。对于双方，一种新的双赢的合作模式，成了他们迫切的话题。目前支付宝与工商等银行，进行着互换“黑名单信息”的沟通方式，支付宝的记录也将作为诚信指数提供给银行，成为银行发放贷款、衡量优质客户的重要依据。支付宝每天产生大量可记录、可监控信息，有巨大的数据库为基础的支付宝，可以成为评判个人诚信体系的重要指标。 4)二者的关系逐步发展为竞合对手。目前，两者正进行着激烈的竞争。银行界试图进军第三方支付，《电子商务发展“十一五”规划》中已经明确指出要鼓励银联、商业银行等机构发展第三方支付业务，这将给第三方支付机构带来很大的压力。而此时，第三方支付也积极采取措施以增加竞争力，例如支付宝近期也在走国际化开拓路径，已经跟日本、美国、澳大利亚等地的三百多家知名百货公司谈好合作，支持12种国际货币的兑换，中国用户只需进入国外百货公司的网站，通过支付宝就可以买到澳大利亚的奶粉、日本的包包等。第三方支付一旦做大，将与银行的网上银行及网上支付抢生意，甚至有可能领取银行牌照，变身成零售银行。 另一方面，二者缺一不可。在网上支付领域里，银行的地位是无法被取代的，第三方支付最终实现资金的存取和转账还要在银行账户进行。同时，鉴于支付宝和淘宝网在中国电子商务市场举足轻重的地位，银行丧失了和支付宝的合作就意味着面临持卡人的抵触，丧失大量客户。 参考文献： [1]杨坚争.电子商务基础与应用[M].西安电子科技大学出版社， 2007.315-317. [2]赵立平.电子商务概论[M].上海：复旦大学出版社，2000. [3]帅青红.电子支付结算系统[M].成都：西南财经大学出版社， 2006. 我国第三方支付与银行的关系探析 —— —以支付宝为例 秦文瑞苏小琳 西南财经大学经济信息工程学院成都611130 摘要：随着国内电子商务的日渐流行，第三方支付作为其主要实现手段，倍受人们的关注。而第三方支付的发展离不开 银行系统的支持，同样以营利为目的，二者如何共存于电子商务市场？本文将以支付宝为例探析二者的关系。 关键词：支付宝第三方支付银行 doi:10.3969/j.issn.1006-8554.2009.09.028 金融管理 41
景德镇陶瓷学院 半导体课程设计报告 设计题目n型半导体材料的设计与性能分析专业班级 姓名 学号 指导教师 完成时间
一﹑杂质半导体的应用背景 半导体中的杂质对电离率的影响非常大，本征半导体经过掺杂就形成杂质半导体，半导体中掺杂微量杂质时，杂质原子的附近的周期势场的干扰并形成附加的束缚状态，在禁带只能够产生的杂质能级。能提供电子载流子的杂质称为施主杂质，相应能级称为施主能级，位于禁带上方靠近导带底附近。 一、N型半导体在本征半导提硅（或锗）中掺入微量的5价元素，例如磷，则磷原子就取代了硅晶体中少量的硅原子，占据晶格上的某些位置。 磷原子最外层有5个价电子，其中4个价电子分别与邻近4个硅原子形成共价键结构，多余的1个价电子在共价键之外，只受到磷原子对它微弱的束缚，因此在室温下，即可获得挣脱束缚所需要的能量而成为自由电子，游离于晶格之间。失去电子的磷原子则成为不能移动的正离子。磷原子由于可以释放1个电子而被称为施主原子，又称施主杂质。 在本征半导体中每掺入1个磷原子就可产生1个自由电子，而本征激发产生的空穴的数目不变。这样，在掺入磷的半导体中，自由电子的数目就远远超过了空穴数目，成为多数载流子（简称多子），空穴则为少数载流子（简称少子）。显然，参与导电的主要是电子，故这种半导体称为电子型半导体，简称N型半导体。 二、P型半导体在本征半导体硅（或锗）中，若掺入微量的3价元素，如硼，这时硼原子就取代了晶体中的少量硅原子，占 据晶格上的某些位置。硼原子的3个价电子分别与其邻近的3个硅原子中的3个价电子组成完整的共价键，而与其相邻的另1个硅原子的共价键中则缺少1个电子，出现了1个空穴。这个空穴被附近硅原子中的价电子来填充后，使3价的硼
CHINA MANAGEMENT INFORMATIONIZATION /有创新要素中，大量怀揣创业梦想的创新人才共同构筑了天使投资发育的温床。而创新人才的供给必定离不开高等教育群落的存在，有了高校群落，尤其是研究型大学的支撑，就可以带来源源不断的创新人才。同时，创新也需要有很好的报酬安排，硅谷的员工持股计划对于吸引创新人才，引燃创业激情，汇聚创新要素都有着巨大的推动作用，如何借鉴这一经验是发展我国天使投资过程中必须解决好的重要问题。 其次，要考虑天使投资人的供给是否充足。天使投资是风险极高的投资，要有一定经济实力的人才可以做，这些人不一定是富豪，但要具备较强的资本运作意识和风险承受能力。因此，打造区域天使投资市场要选择市场经济发达，成功的企业家和创业者聚集的地域。同时，天使投资人是具有天使一样情怀的人，乐于奉献甘愿冒险是天使投资人的共同基因。他们视天使投资为再创业的过程，乐于为创业者提供各种援助。天使投资人在追求较高投资回报的同时，由投资项目有益于社会福利等原因所带来的社会公共效应和创业成功的荣誉感，也是天使投资人普遍追求的效用。因此那些具备较强的社会责任感的人群才是符合条件的潜在天使投资人。 最后，要考虑区域环境是否适宜。天使投资能否得到持续健康发展，高度依赖于区域环境的健康程度，其中包括民间信用水 平的高低，企业群的集聚程度，私人股权转让市场的发达程度，政府的优惠政策力度大小，地域经济结构及发展方向等。 主要参考文献 ［１］王德禄，徐苏涛．创业视角下的天使投资［Ｊ］．科技创新与生产力，２０１３（２）． ［2］程爱华.国外天使投资发展的经验及对民营企业融资的启示［Ｊ］.全国 商情，２００９（5）. ［3］邵坤.探索中国天使投资模式［Ｊ］.资本市场，２０１２（3）. ［4］徐苏涛，胡朋.硅谷天使投资案例研究［Ｊ］.科技创新与生产力，２０１３（4）. ［5］韩宇.风险投资与美国高技术城市的成长［Ｊ］.史学集刊，２０１４（2）.［6］王德禄.硅谷创新的秘诀：天使投资［Ｊ］.中关村，２０１０（4）. ［7］刘洋.天使元年中国天使投资发展研究［Ｊ］.湖北工业大学学报，２０１２（6）. ［8］王晓津.美国创业投资的早期发展［Ｊ］.财经科学，２００５（3）. ［9］李向辉，李艳茹.国外天使投资发展经验及对江苏省的启示［Ｊ］.江苏 科技信息，２０１４（3）. ［10］钟坚.美国硅谷模式成功的经济与制度分析［Ｊ］.学术界，２００２（3）. 第三方支付平台与银行的“软冲突”智慧 —— —以支付宝为例鲁嬴 （北京语言大学信息管理与信息系统系，北京100083） ［摘 要］随着以支付宝为代表的第三方支付平台在公民日常生活中所起到的作用和影响力与日俱增，第三方支付平台与银 行的关系也悄然发生着变化，从最初的“蜜月期”到四大行联手封杀支付宝的传闻再到第三方支付平台频频出招涉足金融之外的多个领域提高自己的“抗打击”能力。本文提出“软冲突”概念，通过对支付宝与银行之间合作与竞争关系由来始末的分析一探双方关系的发展前景。 ［关键词］软冲突；支付宝；银行；沉淀资本金 doi:10.3969/j.issn.1673-0194.2014.23.043 ［中图分类号］F830［文献标识码］A ［ 文章编号］1673-0194（2014）23-0073-03［收稿日期］2014-08-14［基金项目］北京语言大学“北语模课”一期工程教学模式改革项目经费资助。 1 研究背景 随着电子商务的繁荣发展和第三方支付方式的兴起，人们在日常生活中切实享受到第三方支付带来的便利：代购代付、虚拟用品交易、充值，水电费线上支付等。支付宝作为目前国内第三方支付平台的代表，其影响力已经渗透到公民日常生活中的方方面面。然而随着支付宝业务领域的不断扩大，其与以银行为代表的传统金融行业的利益冲突也愈演愈烈。随着２０１４年３月央行对支付宝虚拟信用卡的封杀，对线下“条码支付”方式持消极态度，支付宝与四大行的关系骤然降温。 本文以支付宝和银行之间的关系为讨论话题，首先回顾了支付宝和银行之间的紧密合作；其次在分析了支付宝和银行之 间利益冲突特点的基础上阐述了“软冲突”的定义和含义；最后结合支付宝自身的优势与劣势对其与银行之间的关系进行了展望。 2 支付宝与银行的合作与竞争2.１支付宝的繁荣得益于银行支持 支付宝自２００６年成立并推出自己的网上支付功能以来，一直采取与银行“紧密”合作的姿态。所谓“紧密”体现在两个方面。其一，支付宝的出现帮助银行系统很好地缓解了在线支付客户流失的现象，而这种现象多半是因为各银行金融接口不同而导致的。换句话说支付宝代替银行在网络金融方面，尤其是网络支付方面做了“试水”的尝试，结果有目共睹。其二，支付宝与工商银行合作，通过建立备付金账户的形式，作为第三方对网络交易过程进行信用担保，极大地保证了在线交易的安全性，从而为自己吸引了大量客户。正因为有银行体系的大力支持，支付宝发展初期一帆风顺，加之在第三方支付领域的领先地位，其迅速占领国内市场，最终发展成如今规模。在众多支付宝与银行合作的例证中， !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 中国管理信息化 Ｃhina Management Informationization 2014年12月第17卷第23期 Dec.，2014Vol ．17，No ．23 73
支付宝限额对商业银行的积极影响 经济与工商管理学院花度 摘要：自支付宝平台及余额宝产品推出以来，随着互联网的快速发展，支付宝公司与商业银行在支付、转账等多种业务上存在竞争。本文以新规在2016年正式施行后，支付宝的转账、支付等业务额度受限为例，说明了新规对支付宝的限制，以及新规发行后，商业银行从中受到的积极影响。 关键词：支付宝限额；商业银行；影响 自2010年以来，整个支付市场发生了巨大的变化。据易观智库数据显示，2015年第3季度，中国第三方移动支付市场交易规模达43914亿元。其中支付宝以71.51%的市场份额占据首位；而银联的市场份额仅为0.49%。在第三方支付的冲击下，商业银行在移动支付市场交易规模被严重压缩。 2015年12月28日，央行为规范网络支付业务、防范支付风险，发布了《非银行支付机构网络支付业务管理办法》，该规定将于2016年7月1日正式施行。按此规定，支付宝将支付账户分为三类：Ⅰ类额度1000元/终身，绑定一张银行卡即可；Ⅱ类额度10万元/年，需上传身份证或绑定两张不同的银行卡；Ⅲ类额度20万元/年，在Ⅱ类基础上再加2 项验证即可。三类账户中提到的额度是指用支付宝余额进行转账、消费、购买投资理财等金融类产品共享的额度。虽然银行卡快捷支付、网银、余额宝、蚂蚁花呗等支付工具不算入余额支付额度，但支付宝平台的支付、转账等功能会由于余额支付额度受限。 一、支付宝与商业银行的竞争 （一）支付宝与商业银行负债业务的竞争 支付宝网络技术有限公司是国内第三方支付平台。在网上进行购物支付时，消费者会先将资金汇入支付宝中，待确认收货后，再通过支付宝将资金转给卖家的支付宝账户。在这段时间里，实际上是用户将钱款交给支付宝代为保管，双方之间形成了我国《合同法》第365条下的保管合同关系。［１］从消费者在网上付款时，到确认收货时的时间内，资金存放在支付宝平台的账户上。除消费支付以外，消费者为方便快捷，将资金直接存入支付宝余额，这也形成了支付宝平台的沉淀资金。随着日益扩大的网上消费群体，支付宝沉淀资金压缩了商业银行吸收存款的空间。 商业银行的是以营利为目的，以多种金融负债筹集资金，多种金融资产为经营对象，具有信用创造功能的金融机构。商业银行的业务主要集中在经营存款和贷款业务，即以较低的利率借入存款，以较高的利率放出贷款，存贷款之间的利差就是商业银行的主要利润。支付宝沉淀资金使得商业银行吸收存款减少，压缩了商业银行负债业务，减少了利润空间。同时，支付宝沉淀资金，也相当于存款派生机制中的现金漏损，一部分现金流出银行体系。现金漏损减少了商业银行发放贷款的能力，使存款乘数缩小，派生存款量减少。 （二）支付宝与商业银行资产业务的竞争 支付宝除支付服务外，还提供余额宝等理财服务。2013年6月，支付宝推出账户余额增值服务“余额宝”，通过余额宝，用户不仅能够得到较高的收益，还能随时消费支付和转出，无任何手续费。余额宝是支付宝和天弘基金公司合作推出的一款基金产品。［１］用户将资金转入余额宝后，支付宝平台将使用这笔资金购买天弘公司发行的基金产品。这促进用户将资金存入支付宝余额，并转入余额宝。反观商业银行的理财业务，收益相对余额宝较低，同时存入理财产品的资金不能随时进行支付和转出。当前国内货币基金的年收益率基本在3%到4%之间，而余额宝在推出时，官网宣布的年化收益率达到5.096%，远超国内其他货币基金的平均收益水平，这对商业银行的传统业务和市场地位产生很大冲击。［２］ 二、支付宝限额对支付宝的限制
抗疫过后，必须到了考虑经济的时候了。此次疫情，必将深刻改变中国。一些新的商业需求必然会爆发，并且催生传统商业的迭代升级。那疫情之后，中国社会将有哪些新商业机会？哪些行业可能蓄势爆发？ 心理咨询及心理陪护 疫情期间，很多人天天刷新闻，出现了恐惧、焦虑等情绪。疫情过后，将会有大量的人需要私人心理医生、私人心理陪护。未来，将会有越来越多人的心理需要抚慰，线上咨询服务会逐渐兴起。 餐饮旅游行业 尽管当下，餐饮企业受到的挫折最大。但疫情结束之后的一两个月，餐饮和旅游行业，都会出现大规模的爆发性增长。疫情之后的假期都会出现旅游和餐饮消费旺季。毕竟，该去的地方还得去，该见的朋友还得见。消费的“报复性反弹”是可以预见的。 线上办公软件 很多企业应开始线上办公，尽管对于线上办公仍存在一些争议，但有争议是任何新兴事物的特性。由于成本更低、更灵活，线上办公未来势必会出现爆发浪潮。 在线教育平台
由于学校不能复课，很多在线教育平台迎来爆发的春天。在线教育平台最大的优势是名师优势，疫情之后会加速市场调整，一些听过名师讲课的学生，将再很难将就普通老师，在线教育必然迎来一波爆发期。而且，未来线上教育与线下融合也是必然趋势。 高标准的生活服务 疫情期间，由于人们不愿意去大超市，社区便利店、生鲜电商等企业，都出现了集中爆发。还有一些配送服务，业务都出现井喷。 连锁干洗加盟 疫情过后干洗行业即将迎来大发展。人们经过这次疫情对生活的健康洁净要求将会越来越高，同时对美好生活的向往程度以及享受服务的渴望更高，因此品牌干洗店的健康洗涤方式必将倍受青睐。 品牌干洗店国际规范的洗衣流程会对衣服进行多次消毒处理；使用的清洁溶剂是具有杀菌去污功能的四氯乙烯；高温熨烫和高温烘干机在对衣物进行烘干熨烫的同时亦可再次杀菌灭毒；独立包装避免细菌污染。整个洗涤流程严谨而全面，满足了人们越来越高的“健康洁净”要求。
***系统性能测试报告 V1.0 撰稿人：******* 时间：2011-01-06
目录 1.测试系统名称及测试目标参考 (3) 2.测试环境 (3) 3.场景设计 (3) 3.1测试场景 (3) 3.1测试工具 (4) 4.测试结果 (4) 4.1登录 (4) 4.2发送公文 (6) 4.3收文登记 (8)
1.测试系统名称及测试目标参考 被测系统名称：*******系统 系统响应时间判断原则（2-5-10原则）如下： 1)系统业务响应时间小于2秒，用户对系统感觉很好； 2)系统业务响应时间在2-5秒之间，用户对系统感觉一般； 3)系统业务响应时间在5-10秒之间，用户对系统勉强接受； 4)系统业务响应时间超过10秒，用户无法接受系统的响应速度。 2.测试环境 网络环境：公司内部局域网，与服务器的连接速率为100M，与客户端的连接速率为10/100M 硬件配置： 3.场景设计 3.1测试场景 间
间 间 3.1测试工具 ●测试工具：HP LoadRunner9.0 ●网络协议：HTTP/HTTPS协议 4.测试结果 4.1登录 ●运行1小时后实际登录系统用户数，用户登录后不退出，一直属于在线状态，最 终登录的用户达到9984个；
●响应时间 ●系统资源
服务器的系统资源表现良好（CPU使用率为14%，有15%的物理内存值）。磁盘等其他指标都表现正常，在现有服务器的基础上可以满足9984个在线用户。 4.2发送公文 运行时间为50分钟，100秒后300个用户全部加载成功，300个用户开始同时进行发文，50分钟后，成功发文数量如下图所示，成功发文17792个，发文失败37 个；
哪个行业发展前景好 年轻人心浮气躁，一件工作做的不顺心，就会出现辞职走人的现象。想要跳槽，可是没有公司愿意接纳一份工作干不长久的员工。于是他们又开始了辗转各大招聘市场去找工作。这样的状况持续下去，既不利于公司留住人才，也不利于自己能力的提升。 年轻人找工作，需要深思熟虑，而不能想小孩过家家一样，一不满意，就走人。现在最主要的是，不仅要结合自己的兴趣，同时要兼顾行业发展的前景好坏。转行需要“瞻前顾后”，而不是随意乱找。众所周知，IT行业薪资高，前景好，这也使得很多人投入IT行业。 技术人员缺口越大，在该领域就越好找工作，就越容易实现高新就业。目前IT行业缺口最大的领域之一是网络工程师。随着网络经济的迅猛发展，网络工程在市场经济上的作用已经不可小觑，网络工程师自然也成了一个热门行业。银行、政府、企事业单位等都需要网络维护人员，但是网络工程人才远远不能满足市场的需要。 其二是软件开发工程师。虽然高等院校也开设了众多IT相关的专业和课程，但是学生从学校出来之后普遍存在经验不足的问题，而企业需要的是应用型的人才，因此每年直接进入IT市场的“人才”和企业真正能用的人才在数量上还是有一个很大的差距。
其三是网络营销师。中国的网民数量已经突破五亿，位居全球第一。在如今这样一个数字化、网络化的时代，庞大的网民数量造就了一个充满商机的市场。在时代的影响下，网络营销师成为了近几年蓬勃发展的新兴职位，也成为了众多年轻人择业的新宠。 IT行业的发展趋势和实力都充分证明它不容置疑的领军地位。年轻人可以选择IT行业作为今后的主攻方向，实现自己的人生目标和理想。 （ps：本文章由北大青鸟广安门校区搜集自互联网）
材料物理性能调研报告 学院：材料学院 专业：铸造10-4班 姓名：李俊娟 学号：311006020302
金属塑料 一种集塑料和金属特点于一身的新型材料——“金属塑料”近日由我国科 学家研制成功。有关专家评价说，这种“金属塑料”在很多领域都具有重大的应用和研究价值，可作为纳米、微米加工和复写的优良材料，将来可使汽车部件像塑料一样便宜。 1.块体金属玻璃（玻璃茶杯）与可加工性。 众所周知，从结构上来说，固体物质至少有晶态结构（原子，分子或分子链排列有序）与非晶态结构（原子，分子或分子链排列无序）两大类。而从熔融态冷却形成非晶态结构的固体物质通常又被特指为玻璃态或玻璃。为了从化学组成上区分不同类型的玻璃，在"玻璃"前面又冠以某种定语，如喝酒用的透明玻璃杯一般是氧化物类的，因此称为氧化物玻璃，而塑料通常是由碳-氢分子类聚合物链无序排列而成，因此又称为聚合物玻璃。氧化物或聚合物玻璃在高温的可加工性源于这些材料在高温时发生的软化特性，即可以在"某个温度"以上的"非常宽的温度范围"内能够像揉面团那样进行长时间的无限度变形加工。这里所说的" 某个温度"用专业的术语讲叫玻璃转变温度（Tg），而"非常宽的温度范围"称为"过冷液相区"（ΔTx），过冷液相区ΔTx越宽越好，就越有利于加工成型，而处于该温度范围内的玻璃又称为"过冷液体".在过冷液相区能够停留的时间越长越好，这意味着过冷液体的稳定性好，如果稳定性不好，则意味着过冷液体会很快发生晶化而无法再继续进行加工。从玻璃态而来的过冷液体不同于从高温熔融态的熔体冷却得到的过冷液体，前者可以在一定的时间之内保持一定的形状，这也是玻璃工艺品制作大师们能够进行无模吹制复杂形状工艺品的关键。金属玻璃的出现则还是20世纪60年代初的事。由于金属的特殊性，在常规的冷却条件下，金属合金熔体在冷却过程中总有结晶的倾向，从而形成晶体结构的固体。因此，在20 世纪90年代以前，人们一般是采用至少高达105K/s的冷却速度来冷却合金熔体，以制备出厚度在几十到上百微米的薄带状金属玻璃，这些薄带状玻璃的过冷液相区一般都较窄或几乎没有，其应用领域受到很大的限制。人们研究发现，金属玻璃在多方面都具有其相应晶态合金所不具备的优异物理，力学与化学等特性，因而成为材料与物理学家及工业部门非常感兴趣的开发研究的对象。人们研究金属玻璃形成的一个重要方面是金属合金的玻璃形成能力（或非晶形成能力），一个
发展前景最好的十大职业盘点 在中国，什么职业最好？这个问题再愚蠢不过。职业很难分出优劣。《钱经》换了另外一个角度，将这个棘手问题交付给全国数百位人力资源专家。循着他们对职业的敏感把握，以及跨越行业藩篱的专业素质，我们用大数定律规避了诸多偏见，整理出了以下这张top10的排行榜。 评选因素包括：行业前景、承受压力、进入门槛、福利待遇、供求、上升空间，当然还有《钱经》最关心的——收入。 中国最好的十个职业 1 销售（顾问型销售） 提名理由：在每一个发展正常的公司，销售人员开的车都比老总的好。千万别因为各个行业销售人才缺口的百万量级就一脚踏进来，专家们说，做到了顾问型销售的才是一流，并非人人都能练就九阳神功第十重。 2 it工程师 it 提名理由：无论是熬夜干活的“软件工人”还是闲着数辆保时捷跑车没时间开的金领新贵，这个行业给了每个从业者均等的朝阳曙光，不信？看看那些跟“刀抗母”有关的公司股票吧。 随着人们对电子产品的依赖程度越来越大，掌握电子信息产业技术的人必定身价随之上涨。 压力度★★★★灵活度★★★ 创造性★★★★★从业门槛★★★★ 在这一点上，似乎全世界都达成了共识：根据美国人力资本服务机构dbm公司对其数千名职业顾问和再就业专家进行的调查发现it业的工作需求将大幅反弹。位列增长最快工作排行榜前20位的工作中，有七种工作需要专业计算机技术。 此外随着有闲阶层的出现，网络游戏会越来越发达，相关产业以及服务方面的人才缺口也将达到百万以上。这是一个需要承受巨大工作压力的职业，目前薪资收入一般在15万元左右，那些拥有尖端技术的工程师还会有更好的薪资收入。 3 建筑设计师建筑 提名理由：房地产有多热，建筑设计师就有多热。更何况，他们的衡量标准不是工作量，而是创意。国内高级建筑设计师的年薪在30-100万人民币之间，那些因为一项设计而改变城市的设计师的年薪则不可计也。 地产业的火热使建筑设计师成为价高人稀的“金领”人才。 压力度★★★★灵活度★★★★★创造性★★★★★从业门槛★★★★ 30万-100万
市场上10kv线路无功补偿设备 性能、特点及可靠性分类对比分析 10kv线路分散式无功补偿设备的发展大致经过了固定补偿、搭接式单组投切补偿（单杆安装）、箱式单组投切补偿（双杆安装）和智能化循环自动投切补偿四个阶段。 固定补偿一般是在10kv线路上直接接入高压电容，因为是直接接入，为防止高压电容产生过补，补偿电容容量较小（100kvar以下），只能对线路的无功基荷进行补偿，补偿效果有限。 由于线路负荷随白昼、季节变化较大，为了对峰值无功进行补偿，于是产生了将较大容量的高压电容在高负荷时投入，低负荷时切开的办法。初期是靠人工来进行投切，后发展为用控制器进行单组投切控制。分别为搭接式单组投切补偿和箱式单组投切补偿。 为了进一步提高电容的补偿效率（单组投切只有在无功缺额超过补偿电容值时才进行投切），对无功进行实时动态跟踪补偿，于是出现了智能化循环自动投切补偿方式，实现了无功补偿的智能化控制。 下面，对市场上各类产品的性能、特点及可靠性进行对比分析。 一、固定补偿 这种补偿方式因补偿量太小，因此，补偿效果和经济效益较差。由于是固定补偿，有时会产生过补，对供电质量和供电安全产生一定影响。供电企业已不再采用这种补偿方式。 二、搭接式单组投切补偿 这种补偿方式是对固定补偿进行改造而衍生出来的，它是在固定补偿的基础上增
加了高压开关和控制器，从而实现简单的单组投切控制。采用单杆搭接的安装方式。这类产品的市场价格在3—5万元人民币。 1、它完全是裸露式，主要配套件一一电压互感器、高压开关、补偿电容等 均用不同的支架裸露安装在电杆上，彼此间连接线根据实际空间现场连接。这种产品主要是配套件的组合和堆积，防雨、防潮、防尘、防外力破坏等能力差。 2、该类产品安装的高压开关不具备掉电自动分闸功能，即偶然停电该开关不能自动分闸将补偿电容退出电网，再加电时则未经控制器判断电容器便直接并入电网，易造成电容器损坏，对线路安全运行带来隐患。 3、该产品主要是单组投切，补偿容量较小（200Kvar）以下，节能效果差。 4、该产品控制模式一般为单一电压控制。我们知道，无功缺额大线路电压会降低，但电压偏低却不一定缺少无功。因此，采用单一电压控制不能准确的进行无功补偿。 5、这类产品的控制器不具备数据存储、读取、参数调整及通讯等功能。 6、该类产品由于是各种器件的简单搭接，没有必要的硬件及软件保护措施，因此可靠性较差。 目前，供电企业很少采用这种补偿方式。 三、箱式单组投切补偿 这种补偿方式在结构上进行了一体化设计，将主要的高压器件均安装于一个箱体里。在电压控制的基础上增加了无功等多种控制模式。这类产品的市场价格在7 —9万兀人民币。在供电企业得到了一定的应用。 现将北京亚斯康公司的产品和国内同类产品作对比分析。 无功补偿产品主要性能、特点及可靠性对比分析表
2020年后什么行业发展前景好? 2020年的开局，实在太难了——有人说，我不要这种地狱式的开局，想要重启2020年。每当遭遇不幸，我们就认为这是“时运不佳”，似乎总觉得像打扑克一样，重新摸一手，就能拿到更好的牌。然而很遗憾，事实往往并非如此，生活依然需要继续。那么，2020年后什么行业发展前景好? 疫情过后线上办公软件、心理咨询及心理陪护、餐饮旅游行业、在线教育平台、高标准的生活服务以及干洗店加盟都将迎来“春天”。其中投资小，回报大的当然归干洗店加盟莫属了，下面就来具体聊一聊干洗行业前景。 人们经过这次疫情对生活的健康洁净要求将会越来越高，同时对美好生活的向往程度以及享受服务的渴望更高，因此品牌干洗店的健康洗涤方式必将倍受青睐。 品牌干洗店国际规范的洗衣流程会对衣服进行多次消毒处理；使用的清洁溶剂是具有杀菌去污功能的四氯乙烯；高温熨烫和高温烘干机在对衣物进行烘干熨烫的同时亦可再次杀菌灭毒（例如这次的新型冠状病毒最害怕的就是高温啦）；独立包装避免细菌污染。整个洗涤流程严谨而全面，满足了人们越来越高的“健康洁净”要求。 连锁品牌的干洗店，结合线上收送模式，提供上门服务，让人们的生活更轻松，完全符合人们对“美好生活”的定义。 相信通过此次疫情相信也让很多人意识到努力生活的真正意义就是就是父母健康，儿女可爱，夫妻相濡以沫，但是如今的背井离乡与最初的愿望岂不是背道而驰？
特别是干洗行业投资小，利润高，无囤货，低风险，既能陪在家人身边，也不耽误挣钱养家，同时还能为国家的疫情防护奉献一份自己的力量，一举三得。 东方瑞俪自成立时，就不断发展直营店，已经有多家直营店，利用总部丰富、可靠的经营经验，来帮助加盟店经营，指导加盟店规避经营风险。同时，深知中国洗衣行业的发展状况和程度，自行研发设备、洗衣技术，和加盟商、消费者形成信赖和互助的关系，运营具有稳定的口碑和顾客市场。 温馨提示：投资有风险，加盟需谨慎。
互联网金融对传统商业银行的影响分析——以支付宝为例
摘要 自从改革开放以来，我国的国民经济获得了持续较快的发展。于此同时，企业的贷款融资、公民的个人信贷、公民储蓄理财等各种需求也随之而来。有需求就会有市场，由于我国的金融行业起步时间较晚，因此在突然增长的市场环境面前，我国的金融行业获得了飞速发展的机会。从改革开放到现在，大部分的时间金融行业发展的“黄金时期”。与此同时，对于金融行业的法律体系和市场监管也日益完善。但是，近年来，随着经济和科技的快速发展，公民消费便捷度需求的增加和理财产品灵活多样化需求的增加，各类电子银行和数据金融产品应运而生。以阿里巴巴旗下的“支付宝”为例，其同等本金下的收益系数远远高于银行利息，并且存取便捷，其旗下的理财基金产品也更加多样化。这种数字金融的出现对于传统金融的冲击无疑是巨大的。但是同时，传统金融由于监管到位、体系完善等优势，仍有广阔的发展空间。本文以新形势下的传统金融出发点，系统地分析和研究了传统金融的弊端和所面临的危机，同时以支付宝为例，根据支付宝的特点分析了其自身优势和机遇，继而针对传统商业银行的不足之处提出了相关的改进意见。以期为传统金融适应时代发展和市场需求提供文献资料和理论参考，推动我国金融体系的进一步完善，推动我国金融市场的长期健康发展。 关键词：传统金融；数字金融；金融体系；金融市场
目录 第一章导论 (4) 1.1研究背景 (4) 1.2研究目的与意义 (4) 1.3文献综述 (5) 1.4研究内容及结构安排 (6) 1.5研究方法及技术路线 (6) 1.6创新之处 (7) 第二章互联网金融的理论概述 (7) 2.1互联网金融的概念和特征 (7) 2.2支付宝的运营模式 (9) 2.3本章小结 (9) 第三章支付宝的发展战略研究 (9) 3.1支付宝发展简介和概括 (9) 3.2支付宝发展的优劣势分析 (9) 3.3本章小结 (10) 第四章支付宝对传统商业银行的影响分析 (11) 4.1支付宝给传统商业银行带来的方便 (11) 4.2支付宝给传统商业银行带来的冲击 (11) 4.3传统商业银行应对支付宝挑战的优劣势 (15) 4.4本章小结 (16) 第五章传统商业银行应对支付宝冲击的策略 (17) 5.1转变传统的经营理念和方式 (17) 5.2多元化经营策略 (18) 5.3合作与竞争策略 (20) 5.4本章小结 (21) 第六章结论与展望 (21) 6.1结论 (21) 6.2展望 (22) 参考文献 (22) 附录 (24)