scholarly journals An analysis of design process and performance in distributed data science teams

2019 ◽  
Vol 25 (7/8) ◽  
pp. 419-439
Author(s):  
Torsten Maier ◽  
Joanna DeFranco ◽  
Christopher Mccomb
Keyword(s):  
Software Development ◽  
Data Science ◽  
Team Member ◽  
Semantic Analysis ◽  
Distributed Data ◽  
Team Size ◽  
Distributed Software ◽  
Content Type ◽  
Crowdsourced Data ◽  
And Performance

PurposeOften, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design and psychology contradicts this assumption. This study aims to examine the behavior of teams engaged in data science competitions. Crowdsourced competitions have seen increased use for software development and data science, and platforms often encourage teamwork between participants.Design/methodology/approachWe specifically examine the teams participating in data science competitions hosted by Kaggle. We analyze the data provided by Kaggle to compare the effect of team size and interaction frequency on team performance. We also contextualize these results through a semantic analysis.FindingsThis work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting teams are more likely to win competitions. The semantic analysis revealed differences in forum participation, verb usage and pronoun usage when comparing top- and bottom-performing teams.Research limitations/implicationsThese results reveal a perplexing tension that must be explored further: true teams may experience better performance with higher cohesion, but nominal teams may perform even better on average with essentially no cohesion. Limitations of this research include not factoring in team member experience level and reliance on extant data.Originality/valueThese results are potentially of use to designers of crowdsourced data science competitions as well as managers and contributors to distributed software development projects.

scholarly journals An Analysis of Design Process and Performance in Distributed Data Science Teams

2019 ◽  
Author(s):  
Christopher McComb ◽  
Joanna DeFranco ◽  
Torsten Maier
Keyword(s):  
Software Development ◽  
Data Science ◽  
Team Member ◽  
Semantic Analysis ◽  
Distributed Data ◽  
Team Size ◽  
Distributed Software ◽  
Crowdsourced Data ◽  
And Performance ◽  
Analyze Data

Purpose – Often, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design, and psychology contradicts this assumption. This work examines the behavior of teams engaged in data science competitions. Crowdsourced competitions have seen increased used for software development and data science, and platforms often encourage teamwork between participants.Design/methodology/approach – We specifically examine teams participating in data science competitions hosted by Kaggle. We analyze data provided by Kaggle to compare the effect of team size and interaction frequency on team performance. We also contextualize these results through a semantic analysis.Findings – This work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting teams are more likely to win competitions. The semantic analysis revealed differences in forum participation, verb usage, and pronoun usage when comparing top- and bottom-performing teams.Research limitations/implications- These results reveal a perplexing tension that must be explored further: true teams may experience better performance with higher cohesion, but nominal teams may perform even better on average with essentially no cohesion. A limitation of this research includes not factoring in team member experience level and reliance on extant data.Originality/Value – These results are potentially of use to designers of crowdsourced data science competitions as well as managers and contributors to distributed software development projects.

Diversity climate perceptions and its impact on multicultural team innovation and performance

2020 ◽  
Vol 24 (3) ◽  
pp. 301-318 ◽  
Author(s):  
Raavee Kadam ◽  
Srinivasa A. Rao ◽  
Waheed Kareem Abdul ◽  
Shazi Shah Jabeen
Keyword(s):  
Cultural Diversity ◽  
Cultural Intelligence ◽  
Team Member ◽  
Diversity Climate ◽  
Content Type ◽  
Team Members ◽  
Team Diversity ◽  
Team Innovation ◽  
Climate Perceptions ◽  
And Performance

Purpose This study aims to examine the influence of diversity climate perceptions (DCPs) on team member’s contribution to team innovation and team performance in a multicultural team (MCT). The authors also investigate the moderating effect of cultural intelligence on these relationships. Design/methodology/approach The authors draw upon the interactional model for cultural diversity to build their hypotheses. Data was gathered from 43 teams consisting of 217 members using a structured questionnaire. Ratings were obtained from both team members and supervisors. The data collected was analyzed using structural equation modeling. Findings Results indicated that when team members have positive DCPs, it had a positive impact on their innovation and performance in the team. Cultural intelligence was also found to have a direct impact on team member innovation but not on team member performance. Furthermore, cultural intelligence was found to positively moderate the DCPs – team member performance relationship but not the DCPs – team member innovation relationship. Practical implications Managing diversity is a key concern for organizations worldwide given the exponentially rising cultural diversity within the workforce. This study would enable practitioners to understand that developing positive DCPs and cultural intelligence of team members are critical to the success of MCTs. Originality/value Literature has documented mixed results pertaining to team diversity and its effect on performance, resulting in scholars urging the need to explore how the negative effects of team diversity can be mitigated. This research establishes that positive DCPs and cultural intelligence as two key factors contributing to the performance of MCTs.

Knowledge4Scrum, a novel knowledge management tool for agile distributed teams

VINE ◽  
2014 ◽  
Vol 44 (3) ◽  
pp. 394-419 ◽  
Author(s):  
Roopesh Kevin Sungkur ◽  
Mayvin Ramasawmy
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Software Development ◽  
Knowledge Creation ◽  
Team Member ◽  
Management Tool ◽  
Distributed Teams ◽  
Content Type ◽  
Team Members ◽  
Distributed Development

Purpose – The purpose of this paper is to propose Knowledge4Scrum, a novel knowledge management tool for agile distributed teams. Agile software development (ASD) refers to a group of software development methodologies based on iterative development, where requirements and solutions evolve through collaboration between self-organizing cross-functional teams. The two most widely used methodologies based on the agile philosophy are scrum and extreme programming. Whichever methodology is considered, agile teams usually consist of few members and are collocated under the same roof. However, nowadays, agile practices for distributed development are gaining much momentum. The main reasons behind such practice are cheaper skilled labour, minimizing production cost, reducing time to market and increasing the quality and performance of projects. Along with the benefits obtained through globally distributed development, there are, however, many difficulties faced by various organisations. These problems are caused mostly due to distance, time and cultural differences. To meet up with the level of complexity of projects, ASD also has to keep up with many challenges, especially in cases of distributed teams. Four major challenges have been identified. First, the introduction of global software development entails a number of difficulties, especially related to knowledge sharing. For instance, lack of transparency is frequently observed within such teams, whereby a team member is totally unaware of the activities of his/her colleagues. Second, the unavailability of team members due to time zone differences adds up to the list of problems confronted by distributed teams. Third, there can be misunderstanding amongst the team member due to communication problems, especially in cases where the mother language of the team members is different. Fourth, a common issue faced by distributed teams is the loss of knowledge when an employee resigns from his/her post. Design/methodology/approach – Based on the main problems outlined above, what has been proposed is Knowledge4Scrum, a novel knowledge management tool for agile distributed teams. Knowledge4Scrum will act as a global repository for knowledge sharing in Scrum distributed teams with the possibility of creating new knowledge through data mining techniques. Valid past projects data have been collected to train and test the data mining models. The research also investigates the suitability of knowledge management in Scrum distributed teams to address the various challenges addressed above. Findings – Knowledge4Scrum supports the four knowledge management processes, namely, knowledge creation/acquisition, knowledge storage, knowledge dissemination and knowledge application. It has been found that the aforementioned tool satisfactorily addressed issues of distance, time and cultural differences that crop-up in distributed development teams. Data mining has been the main aspect for the knowledge creation and application processes, whereby new knowledge has been determined by examining and extracting patterns from existing data found in the repository. Originality/value – A major feature of the Knowledge4Scrum tool lies in the knowledge creation and application section, where a number of data mining techniques have been utilised to identify trends and patterns in past data collected. When compared to the COnstructive COst MOdel to estimate project duration, Knowledge4Scrum gives more than satisfactory results. Such functionalities will actually help managers for future project planning and in decision-making.

scholarly journals Trust and performance in business teams: a meta-analysis

2020 ◽  
Vol 26 (5/6) ◽  
pp. 287-300 ◽  
Author(s):  
Amy M. Morrissette ◽  
Jennifer L. Kisamore
Keyword(s):  
Team Performance ◽  
Meta Analysis ◽  
Performance Criteria ◽  
Team Size ◽  
Criterion Measure ◽  
Cross Sectional ◽  
Content Type ◽  
And Performance ◽  
The Relationship ◽  
Team Trust

Purpose The purpose of this study is two-fold. First, the nature of the relationship between team trust and team performance in the business context is determined. Second, both team design (team size and team type) and methodological moderators (source of criterion measure and study date) of the relationship are assessed. Design/methodology/approach A random-effects meta-analysis was performed on published and unpublished empirical studies. Subgroup moderator analyses were conducted using Cochran’s Q. Continuous moderator analyses were conducted using meta-regression. Findings Data from 55 independent studies (3,671 teams) were pooled. Results indicated a large, positive relationship between team trust and team performance in real business teams. Further analyses indicated that the relationship was significantly moderated by business team type, team size and source of criterion measure. Research limitations/implications Results indicate that different team types, sizes and performance criteria should not be treated as equivalent. Results are based on cross-sectional research and can only be generalized to business teams. Practical implications Managers should be attentive to trust issues in work teams, as they may portend future performance problems or mirror other organizational issues that affect team performance. Team function and size predict how team trust is related to team performance. Originality/value The present study answers a call by Costa et al. (2018) for additional investigation of moderators of the trust-performance relationship in teams using a quantitative review of studies.

I will follow (when I need to)

2016 ◽  
Vol 45 (4) ◽  
pp. 707-723
Author(s):  
Jeroen P. de Jong ◽  
Petru L. Curseu
Keyword(s):  
Team Performance ◽  
Personality Trait ◽  
Team Member ◽  
Team Leadership ◽  
Content Type ◽  
Intergroup Competition ◽  
And Performance ◽  
Indirect Relationship ◽  
Conditional Indirect Effects ◽  
Different Levels

Purpose – The purpose of this paper is to investigate if the personality trait of desire for control over others (DFCO) matters to team leadership and performance, and how commitment to the leader mediates this relationship. Furthermore, the authors study whether intergroup competition moderates this indirect relationship. Design/methodology/approach – The authors test hypotheses for mediation and moderation using a sample of 78 groups and their leaders. Commitment to the leader and intergroup competition were measured at the team member level, while DFCO and team performance was rated by the team leader. Bootstrapping was used to assess the significance of the (conditional) indirect effects. Findings – The results show that leader’s DFCO does not relate to team performance through commitment to the leader. Leader’s DFCO only relates negatively to team performance through commitment to the leader when the team operates in a context with little or moderate intergroup competition. In a highly competitive environment, however, leader’s DFCO does little damage to team performance. Originality/value – This research is the first study to focus on DFCO as a personality trait of a group leader. In doing so, it adds to the continuing debate about leader personality and context, as well as the ongoing study on how subordinates respond to different levels of control over decisions in groups.

Does traveler satisfaction differ in various travel group compositions?

2018 ◽  
Vol 30 (3) ◽  
pp. 1663-1685 ◽  
Author(s):  
Xun Xu
Keyword(s):  
Semantic Analysis ◽  
Online Reviews ◽  
Influential Factors ◽  
Online Review ◽  
Content Type ◽  
Performance Factors ◽  
Segmentation Strategy ◽  
The Usa ◽  
And Performance ◽  
Group Compositions

Purpose This study aims to investigate the online customer review behavior and determinants of overall satisfaction with hotels of travelers in various travel group compositions. Design/methodology/approach The author collected data from online reviews of travelers in various travel group compositions from 600 hotels in 100 of the largest cities in the USA from Booking.com and used latent semantic analysis (LSA) to identify the positive and negative factors from online reviews of travelers in various travel group compositions. Then, text regression was used to determine the influential factors of overall satisfaction of travelers in various travel group compositions. Findings It was found in this study that not all the positive and negative textual factors mined from travelers’ online reviews significantly influenced their overall satisfaction. In addition, the determinants of traveler satisfaction were different when travelers were in different travel group compositions. Research limitations/implications The author found similar online review behavior, but different basic, excitement and performance factors of travelers in different travel group compositions. Practical implications This study helps hoteliers understand customers’ perception of the specific attributes of their products and services, which provides a guideline for businesses to design the priority rule to improve these corresponding attributes and use market segmentation strategy when dealing with customers in different travel group compositions. Originality/value The author examined and compared the online review behavior and determinants of satisfaction using the factors mined from online reviews between travelers in various travel group compositions. This study combined customer ratings with textual reviews and predicted customer ratings from the factors extracted from textual reviews using LSA and text regression.

Job qualifications study for data science and big data professions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwah Ahmed Halwani ◽  
S. Yasaman Amirkiaee ◽  
Nicholas Evangelopoulos ◽  
Victor Prybutok
Keyword(s):  
Big Data ◽  
Data Science ◽  
Semantic Analysis ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Job Descriptions ◽  
Content Type ◽  
Professional Groups ◽  
Curriculum Guidelines ◽  
Usage Patterns

PurposeThe lack of clarity in defining data science is problematic in both academia and industry because the former has a need for clarity to establish curriculum guidelines in their work to prepare future professionals, and the latter has a need for information to establish clear job description guidelines to recruit professionals. This lack of clarity has resulted in job descriptions with significant overlap among different related professional groups. This study examines the industry view of five professions: statistical analysts (SAs), big data analytics professionals (BDAs), data scientists (DSs), data analysts (DAs) and business analytics professionals (BAs). The study compares the five fields with the unified backdrop of their common semantic dimensions and examines their recent dynamics.Design/methodology/approach1,200 job descriptions for the five Big Data professions (SA, DS, BDA, DA and BA) were pulled from the Monster website at four points in time, and a document library was created. The collected job qualification records were analyzed using the text analytic method of Latent Semantic Analysis (LSAs), which extract topics based on observed text usage patterns.FindingsThe findings indicated a good alignment between the industry view and the academic view of data science as a blend of statistical and programming skills. This industry view remained relatively stable during the 4 years of our study period.Originality/valueThis research paper builds upon a long tradition of related studies and commentaries. Rather than relying on subjective expertise, this study examined the job market and used text analytics to discern a space of skill and qualification dimensions from job announcements related to five big data professions.

scholarly journals Selected implications of a hyper-connected world on pavement engineering

2020 ◽  
Vol 13 (6) ◽  
pp. 673-678
Author(s):  
Wynand Jacobus van der Merwe Steyn
Keyword(s):  
Data Science ◽  
Industrial Revolution ◽  
Cost Benefit ◽  
Physical World ◽  
Management Systems ◽  
Life Data ◽  
Pavement Engineering ◽  
Potential Benefits ◽  
And Performance ◽  
Lifecycle Management

AbstractThe world is becoming a hyper-connected environment where an abundance of data from sensor networks can provide continuous information on the behaviour and performance of infrastructure. The last part of the 3rd Industrial Revolution (IR) and the start of the 4th IR gave rise to a world where this overabundance of sensors, and availability of wireless networks enables connections between people and infrastructure that was not practically comprehensible during the 20th century. 4IR supports the datafication of life, data science, big data, transportation evolution, optimization of logistic and supply chains and automation of various aspects of life, including vehicles and road infrastructure. The hyper-connected 4IR environment allows integration between the physical world and digital and intelligent engineering, increasingly serving as the primary lifecycle management systems for engineering practitioners. With this background, the paper evaluates a few concepts of the hyper-connected pavement environment in a 4IR Digital Twin mode, with the emphasis on selected applications, implications, benefits and limitations. The hyper-connected world can and should be managed in the pavement realm to ensure that adequate and applicable data are collected regarding infrastructure, environment and users to enable a more efficient and effective transportation system. In this regard, and planning for future scenarios where the proliferation of data is a given, it is important that pavement engineers understand what is possible, evaluate the potential benefits, conduct cost/benefit evaluations, and implement appropriate solutions to ensure longevity and safety of pavement infrastructure.

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