**4. Summary and Conclusions**

The practical implications of our chapter are, indeed, numerous from the eight practical propositions to the six design FSS aspects regarding adaptations to Task Complexity and is‐ sues having to do with forecaster confidence. We summarize these here next. First, fitting technology support to task characteristics can provide a useful mechanism for identifying gaps between system functionality and user needs. Understanding task characteristics and corresponding support needs will enable FDSS designers to create systems that better suit and adapt to user needs. Second, a methodical integration of task and support technologies can lead to greater user commitment, thereby reducing forecaster's tendency to make delete‐ rious *ad hoc* adjustments. Task-technology fit can enable identification of functions for which human intervention can be problematic and thereby restrict or guide selection towards im‐ proved choice [65, 93]. For instance, systems that complement limitations of human informa‐ tion processing (HIP) may improve decision maker performance [40] because they mitigate cognitive overload that constrains human performance on complex tasks [94]. Finally, a well-designed and optimally utilized FSS has a strong positive impact on individual per‐ formance and system adoption [20]. From an organizational perspective, this can have measurable positive implications for return on investments [95-96].

**Feature Description and Implementation as in C&A Operationalization**

Designing Effective Forecasting Decision Support Systems: Aligning Task Complexity and Technology Support

A last observation that is 90% more than the highest or

Changing Basic Trend (CB) Underlying trend that is changing over the long run. Judgmental identification -

Suspicious Pattern (Sus.) Series that show a substantial change in recent pattern. Judgmental identification -

Level Discontinuities (LD) Changes in the level of the series (steps) Judgmental identification -

the series. *Growth* exerts an upward force. *Decay* exerts a downward force. *Supporting* forces push in direction of historical trend. *Opposing* forces work against the trend. *Regressing* forces work towards a mean. When uncertain,

trend is growing while causal forces are decay, then

adjusted data. If coefficient is >0.2, the series is flagged

Last observation deviates substantially from previous data. Judgmental identification -

Judgmental identification -

http://dx.doi.org/10.5772/51255

189

Automatic identification -

Automatic identification -

Automatic identification -

Automatic identification -

Judgmental identification -

Judgmental identification -

Judgmental identification -

Judgmental assessment for

Automatic identification -

C&A\*

C&A

C&A

C&A

C&A

C&A

C&A\*

C&A

C&A\*

C&A

C&A\*

C&A

this study

C&A

Functional Form (FF) Expected pattern of the trend of the series. Can be multiplicative or additive.

data.

Near a Previous Extreme

Unusual Last Observation

(Ext.)

(ULO)

**Table A.**

past data.

regress.

direction.

Basic Trend (BT) Direction of trend after fitting linear regression to past

Recent Trend (RT) The direction of trend that results from fitting Holt's to

Outliers (Out.) Isolated observation near a 2 std. deviation band of linear

Recent Run Not Long (RR) The last six period-to-period movements are not in same

Irrelevant Early Data (Irr.) Early portion of the series results from a substantially different underlying process.

Causal Forces (CF) The net directional effect of the principal factors acting on

forces should be *unknown*.

Trend Conflict (TC) If recent trend conflicts with causal forces, e.g. recent

a trend conflict is flagged.

Trend Variation (TV) Standard deviation divided by the mean for the trend

as being uncertain.

110% lower than lowest observation.

From a forecasting perspective, this study has yielded several insights to forecaster behavior and implications for FDSS design. We find that little has been done in the forecasting litera‐ ture by way of developing a formal taxonomy for forecasting tasks. The principal reason for this is that a taxonomy for forecasting tasks essentially depends upon a codification of series complexity. We have endeavored to begin this classification work [52]. Our framework pro‐ vides an initial attempt to do so in the domain of time series forecasting. Researchers in vari‐ ous other domains may find explorations of similar classifications to be beneficial in making recommendations for systems design in their own domains. Further, we find that forecast‐ ers' behaviors regarding direction and magnitude of these adjustments is impacted by com‐ plexity of the forecasting task, thereby underscoring the value of parsing out simple from complex tasks. Finally, considering the above contributions, we recommend the need for congruence between system features and task features. Our research, in some aspects, is ex‐ ploratory in nature and further work is required to solidify this research stream.



#### **Table A.**

**4. Summary and Conclusions**

188 Decision Support Systems

**Appendices**

Coefficient of Variation

Regression T-Statistic

(CV)

(T-Stat)

The practical implications of our chapter are, indeed, numerous from the eight practical propositions to the six design FSS aspects regarding adaptations to Task Complexity and is‐ sues having to do with forecaster confidence. We summarize these here next. First, fitting technology support to task characteristics can provide a useful mechanism for identifying gaps between system functionality and user needs. Understanding task characteristics and corresponding support needs will enable FDSS designers to create systems that better suit and adapt to user needs. Second, a methodical integration of task and support technologies can lead to greater user commitment, thereby reducing forecaster's tendency to make delete‐ rious *ad hoc* adjustments. Task-technology fit can enable identification of functions for which human intervention can be problematic and thereby restrict or guide selection towards im‐ proved choice [65, 93]. For instance, systems that complement limitations of human informa‐ tion processing (HIP) may improve decision maker performance [40] because they mitigate cognitive overload that constrains human performance on complex tasks [94]. Finally, a well-designed and optimally utilized FSS has a strong positive impact on individual per‐ formance and system adoption [20]. From an organizational perspective, this can have

From a forecasting perspective, this study has yielded several insights to forecaster behavior and implications for FDSS design. We find that little has been done in the forecasting litera‐ ture by way of developing a formal taxonomy for forecasting tasks. The principal reason for this is that a taxonomy for forecasting tasks essentially depends upon a codification of series complexity. We have endeavored to begin this classification work [52]. Our framework pro‐ vides an initial attempt to do so in the domain of time series forecasting. Researchers in vari‐ ous other domains may find explorations of similar classifications to be beneficial in making recommendations for systems design in their own domains. Further, we find that forecast‐ ers' behaviors regarding direction and magnitude of these adjustments is impacted by com‐ plexity of the forecasting task, thereby underscoring the value of parsing out simple from complex tasks. Finally, considering the above contributions, we recommend the need for congruence between system features and task features. Our research, in some aspects, is ex‐

ploratory in nature and further work is required to solidify this research stream.

**Feature Description and Implementation as in C&A Operationalization**

Standard deviation divided by the mean for the trend

The t-statistic for linear regression. If T-statistic is greater than abs(2), the series is classified as having a significant

Automatic identification -

Automatic identification -

C&A

C&A

adjusted data.

basic trend.

measurable positive implications for return on investments [95-96].
