### Table 3: A Framework Of Rigor Related To The Stages Of A Theory-Building Research Life-Cycle

"... In PAGE 11: ... A research life-cycle model is useful in understanding the processes of systems stages of theory-generation and enhancement and to define assessments of quality and rigor, at each stage. The lifecycle concept used to frame Table3 was adapted from Galliers apos; (1992) representations of alternative research models, Dey apos;s (1999) discussion of stages of research and models of deductive and inductive research approaches (Babbie, 2001; Eisenhardt, 1989; Yin, 1994). Of course, any lifecycle model is an over-simplified reduction of the research process.... In PAGE 12: ... This subjectivity only remains unchallenged because a succession of socially-constructed claims for truth have accumulated through custom and practice in the natural sciences and these have been adopted uncritically by IS researchers. Table3 presents a summary of the role of subjectivity and rigor in positivist vs. interpretive research, classified by the stages of the research life-cycle.... ..."

### Table 2: Some Verbs of Ful llment One of the applications of the theory developed in this paper is to systematically give a rigorous semantics to what I call the verbs of ful llment. These are verbs such as \obey, quot; 19

1993

Cited by 37

### Table 1: Comparison of SD, Galerkin, and MC for 8 site lattice these results, it is uncertain if this approach will have problems for large systems at very high order. The reason is that by solving only a subset of the SD equations, we ignore more and more equations, the additional inconsistent ones, as we go to higher order. But these equations are relations that must be obeyed by the exact solution to the di erential equa- tions. By discarding, these equations, we have introduced an uncontrolled approximation. Ignoring these low order equations means that there is no guarantee that we will converge to the exact solution. As a practical matter, though, we rarely truncate above 4th or 6th order, so that this loss of a \rigorous quot; notion of convergence might not be a real problem, and in fact, may be a small price for the simplicity of this approach. For many examples, it has successfully captured many of the features of the truncated theory. In the next section, we outline how to de ne a rigorous notion of convergence in the lattice Green apos;s function approach. It will allow us to control the approximations in much the same way as the Galerkin method.

### Table 3. Rigorous bounds on tc for various algorithms.

"... In PAGE 25: ...Table3 , with lower bounds rounded down and upper bounds rounded up. The assumptions involved in this are that the machine used correctly computes double precision floating point operations to the accuracy that it should, and that there is no error in our programming.... ..."

Cited by 1

### Table 3. Rigorous bounds on tc for various algorithms.

"... In PAGE 25: ...Table3 , with lower bounds rounded down and upper bounds rounded up. The assumptions involved in this are that the machine used correctly computes double precision floating point operations to the accuracy that it should, and that there is no error in our programming.... ..."

Cited by 1

### Table 2. Rigorous design (i.e.

"... In PAGE 8: ... Modelling, designing and using generic, composable, open source, and reusable components appear from this table very helpful towards the goal of building systems of systems that can be easily validated and assessed. Designing architectures/infrastructures for dependability Another perspective is related to coping with how to? ( Table2 ). Here multiple facets of dependability raise many issues.... ..."

### Table 3. Rigorous bounds on tc for various algorithms.

"... In PAGE 25: ...Table3 , with lower bounds rounded down and upper bounds rounded up. The assumptions involved in this are that the machine used correctly computes double precision floating point operations to the accuracy that it should, and that there is no error in our programming.... ..."

### Table 4-5. Sampling Timeframe and Sampling Design Rigor Requirements.

"... In PAGE 62: ... Specify the characteristics that define the population of interest. Table4 -1. Characteristics that Define the Population of Interest.... In PAGE 62: ...s a region distinctly marked by some physical features (i.e., volume, length, width, and boundary). Table4 -2. Geographic Areas of Investigation.... In PAGE 63: ... The DQO Team must systematically evaluate process knowledge, historical data, and plant configurations to present evidence of a logic that supports alignment of the population into strata with homogeneous characteristics. Table4 -3. Strata with Homogeneous Characteristics.... In PAGE 63: ... Decision units may be remediation units or risk units.) Table4 -4. Spatial Scale of Decision Making.... In PAGE 64: ...Rev. 1 A-28 Table4 -5a. Consequences, Resampling Access, and Sampling Design Rigor Requirements.... In PAGE 64: ... Determine when to collect data. Table4 -6. When to Collect Data.... In PAGE 64: ... For example, to regulate water quality, it would be useful to set a scale of decision making that limits the time between sampling events, which would minimize the potential adverse effects in case the water quality was degraded between sampling events. Table4 -7. Temporal Scale of Decision Making.... In PAGE 65: ... Identify practical constraints on data collection. Table4 -8. Practical Constraints on Data Collection.... ..."

### Table 1. We have not a rigorous proof but the experiments show us that = ^

in Contents

"... In PAGE 16: ...940559585231717 1 hexahedra (2) b;2 0.9683415044643198 1 Table1 : Upper and lower bounds for the constant ( ), 2 0; 1 2 with respect to di erent triangulations. References [1] B.... ..."